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Natural Language Processing
for the Working Programmer
Daniël de Kok
Harm Brouwer
Natural Language Processing for the Working Programmer
Daniël de Kok
Harm Brouwer
Copyright © 2010, 2011 Daniël de Kok, Harm Brouwer
Some rights reserved. This book is made available under the Creative Commons Attribution 3.0 License (CC-BY). This license is available
Table of Contents
Preface ...................................................................................................................... vii
Acknowledgements .............................................................................................. vii
1. Introduction .............................................................................................................. 1
Welcome ............................................................................................................. 1
What is natural language processing? ....................................................................... 1
What is Haskell? .................................................................................................. 1
What you need ..................................................................................................... 2
Ready, set, go! ..................................................................................................... 2
2. Words ..................................................................................................................... 4
Introduction ......................................................................................................... 4
Playing with words ............................................................................................... 4
From words to sentences ........................................................................................ 7
A note on tokenization ......................................................................................... 10
Word lists .......................................................................................................... 12
Exercises ................................................................................................... 16
Storing functions in a file ..................................................................................... 16
Word frequency lists ............................................................................................ 17
Monads ............................................................................................................. 20
Reading a text corpus .......................................................................................... 22
3. N-grams ................................................................................................................. 25
Introduction ........................................................................................................ 25
Bigrams ............................................................................................................. 25
Exercises ................................................................................................... 29
A few words on Pattern Matching .......................................................................... 30
Collocations ....................................................................................................... 30
From bigrams to n-grams ..................................................................................... 31
A declarative definition of ngrams .................................................................. 32
A monadic definition of ngrams ..................................................................... 33
Exercises ................................................................................................... 35
Lazy and strict evaluation ..................................................................................... 36
Suffix arrays ....................................................................................................... 36
Exercises ................................................................................................... 44
Markov models ................................................................................................... 45
Implementation ........................................................................................... 46
4. Distance and similarity (proposed) .............................................................................. 48
5. Classification .......................................................................................................... 49
Introduction ........................................................................................................ 49
Naive Bayes classification .................................................................................... 50
Maximum entropy classification ............................................................................. 50
Introduction ................................................................................................ 50
6. Information retrieval (proposed) ................................................................................. 53
7. Part of speech tagging .............................................................................................. 54
Introduction ........................................................................................................ 54
Exercises ................................................................................................... 54
Frequency-based tagging ...................................................................................... 54
Evaluation .......................................................................................................... 58
Transformation-based tagging ................................................................................ 59
Exercises ................................................................................................... 67
8. Regular languages (proposed) .................................................................................... 69
9. Context-free grammars (Proposed) .............................................................................. 70
10. Performance and efficiency (proposed) ...................................................................... 71
List of Figures
3.1. Constructing a suffix array ...................................................................................... 37
3.2. Linear search step ................................................................................................. 40
3.3. Binary search step ................................................................................................. 41
5.1. Linear and non-linear classifiers ............................................................................... 50
5.2. Two competing models .......................................................................................... 52
List of Tables
7.1. Performance of the frequency tagger ......................................................................... 59
List of Equations
2.1. Type-token ratio ................................................................................................... 16
3.1. Difference between observed and expected chance ...................................................... 30
3.2. Pointwise mutual information .................................................................................. 31
3.3. Estimating the probability of a sentence .................................................................... 45
3.4. The probability of a sentence as a Markov chain ......................................................... 45
3.5. Approximation using the Markov assumption ............................................................. 46
3.6. The conditional probability of a word using the Markov assumption ............................... 46
3.7. The probability of a sentence using a bigram model ..................................................... 46
5.1. Calculating the empirical value of a feature ............................................................... 51
5.2. Calculating the expected value of a feature ................................................................ 51
5.3. Constraining the expected value to the empirical value ................................................. 51
7.1. Transformation rule selection criterion ...................................................................... 60
We would like to thank:
SyncRO Soft Ltd. for their <oXygen/> XML Editor [], which made
writing of this book very pleasant.
The Computational Linguistics group [
onderzoek/compuling/index] at the Center of Language and Cognition Groningen [http://] for their encouragement and support.
But most of all, for introducing us to natural language processing in the first place.
Chapter 1. Introduction
The Internet and the World Wide Web have changed mankind, forever. It is to early too tell, but their
impact may be as great as the combustion engine or the introduction of electric devices. The Internet
gave universal access to information, not just information that broadcasters or newspapers thought
that was important, but information that interests the 'websurfer'. However the Internet is not a one-
way street, every Internet user is also a producer: people make websites, maintain blogs, post tweets,
and socialize via social networks.
Since a substantial part of the world has Internet access, and every user is also a producer, there is
an enormous amount of information available. Some of it is of peer-reviewed and of a high quality,
most of it is unchecked and biased. Still, every single piece of information can contain valuable
information. For instance, as a vacuum cleaner producer, you might think that social media are not
so interesting. However, the contrary is true: between billions of messages there may be hundreds
expressing sentiment about your product. Such messages can answer questions about how your brand
is conceived, what problems people commonly have with your product, etc.
Obviously, it is out of anyone's reach to manually analyze a significant portion of the information
that is available on the Internet. The amount is just too overwhelming. Classic data analysis tools
may not suffice either, most of the information is seemingly unstructured, and consist of blobs of
natural language sentences. However, language is also structure. It is just not the kind of structure
that computers can normally deal with. Computers deal with neat XML files, fragments of JSON, or
comma separated values. No, it is the structure that we humans use to convey and transfer meaning.
This book is about that type of information. We will go into many of the techniques that so-called
computational linguists use to analyze the structure of human language, and transform it into a form
that computers work with.
What is natural language processing?
What is Haskell?
Haskell is a static, pure, lazy, functional language. Gee, that sounds an awful lot like buzzword lingo!
That may well be, but these properties make Haskell a very effective language, and sometimes a bit
odd. These properties are also opposite to most mainstream languages. For instance, Java is static,
impure, eager, and not functional, Ruby is dynamic, impure, eager, and only functional at times. While
it is hard to give an accurate definition of every characteristic, we will try anyway.
Functional: Haskell puts an emphasis on functions and treats computation as the evaluation of
functions. This in contrast to so-called imperative languages, that specify an order of instructions. As
such, Haskell functions very often resemble mathematical functions closely. Being functional also has
practical implications. For instance, iteration is accomplished by means of recursion, and functions
are also values and can be passed to other functions.
Pure: Haskell is a pure language, in that functions do not have side-effects. This means that existing
values cannot be changed, since changing data would be a side-effect of a function. It also guarantees
that the evaluation of a function will always result in the same value given the same function
Lazy: As a lazy language, Haskell only evaluates expressions when needed. Say you just implemented
a function that gives a list of all prime numbers. In a strict language, the function that makes the list
will never terminate (since there are always more prime numbers). Haskell, on the contrary, will only
evaluate this function when and as much as necessary. As long as you take a finite number of primes
from the list, the program will happily terminate.
Static: Haskell programs are compiled before they can run. This means that the Haskell compiler will
catch many errors for you at compile-time, rather than finding them when your program is used in
production. Additionally, Haskell does type-inference. This means that the Haskell compiler can find
out the types of values most of the times, and you do not need to type-annotate every value.
If you have prior programming experience in an imperative or eager language, Haskell can feel a bit
odd in the beginning. Don't worry, you will feel warm and fuzzy eventually!
You may ask why we chose Haskell as the main programming language for this book, rather than
a more mainstream language. During our own experiences developing natural language processing
tools, we noticed that very many natural language processing tasks are relatively straightforward data
transformations. Haskell is a language that is exceptionally good at data transformations. First of all,
because it has higher order functions (functions that take functions as an argument) that traverse lists,
sets, mappings, etc. Second, Haskell makes it easy to construct more complex transformations out of
simple transformations.
Although this book does not provide a full introduction to the Haskell programming language,
we try to cover Haskell concepts extensively when required. If you require more background on
certain concepts, we recommend you to consult the book Learn Haskell for Great Good! [http://]
What you need
To work with this book, you need the Haskell Platform and a text editor. The Haskell Platform is
available for Mac OS X, Windows, and Linux at: Download the
package for your platform, and install it.
If you do not use one of these platforms, not all is lost. First of all, you should download the ghc
Haskell compiler. If you operating system provides a ports system or package manager, use it to locate
and install a ghc package. If your operating system does not provide a port or package for ghc, you
can still try to download a binary distribution from the GHC website [].
Once you have installed and set up ghc, install the packages of the Haskell Platform.
For the text editor, pick any editor you find comfortable, as long as it saves files as plain text.
Programming Haskell becomes more comfortable if you have an editor with syntax highlighting and
interpreter integration. Your authors prefer the Emacs editor with haskell-mode. haskell-mode provides
good support for highlighting, and Haskell code formatting. Besides that, Emacs allows you to run the
ghci Haskell interpreter within the editor.
Some examples in the book use corpora. These corpora are available at:
Ready, set, go!
You will probably want to get acquainted with the ghci Haskell interpreter. It allows you
to try Haskell expressions and get immediate feedback. On UNIX systems, type ghci -
XNoMonomorphismRestriction in a terminal to launch the interpreter. You will be greeted with a
prompt that resembles the following:
$ ghci -XNoMonomorphismRestriction
GHCi, version 7.0.2: :? for help
Loading package ghc-prim ... linking ... done.
Loading package integer-gmp ... linking ... done.
Loading package base ... linking ... done.
Loading package ffi-1.0 ... linking ... done.
On Windows, choose Start -> All Programs -> Haskell Platform -> WinGHCi. The first time,
you should set the NoMonomorphismRestriction option that is required for some examples.
Do this by going to the following item: File > Options > GHCi Startup. Then append -
XNoMonomorphismRestriction in the field (make sure that there is a space before this addition). Click
OK, and restart WinGHCi.
Chapter 2. Words
Words are the most fundamental building blocks of our language. Although they may look simple
on the surface, they are very ingenious devices that pack not only meaning, but also grammatical
information. For our purposes, we will say that a word consists of a stem and affixes. Let's look at
three simple sentences:
I walk.
John walks.
Jack walked.
All three sentences contain some 'form' of walk. We say that these instances are all inflections of the
verb walk. The part of the inflections that is shared (walk) is what we call the stem. The parts that are
not common are named affixes. We inflect verbs to indicate tense (present, past, etc.), the person of
the verb's subject (first, second, and third), and the number (singular or plural). The affix s in John
walks, for instance, tells us (in combination with the subject John) that the verb walk is in present
tense, third person singular.
Other types of words have inflections as well. For example, we inflect nouns to distinguish singular
and plural:
I saw one duck. I saw two ducks.
Up to this point, we have just seen one kind of affix: one that is glued to the end of the word. There
are actually many types of affixes. For now, you should only know about two:
Prefix: appears in front of the stem. For example, unbelievable.
Suffix: appears after the stem. For example, ducks.
Now, with that out of the way, let's get some work done.
Playing with words
Written words consist of characters. We can write down characters in Haskell with single quotes. If
you type in a character in ghci, it will simply echo back the character:
Prelude> 'h'
This is all that ghci does, it evaluates whatever you type. A character evaluates to... a character. We
confirm that Haskell agrees with us that this actually a character by asking the type with :type or its
shorthand :t:
Prelude> :type 'h'
'h' :: Char
Great. Haskell indeed confirms that 'h' is a character, or in Haskell's words: that 'h' is of type Char.
Not all that practical with the small amount of single-lettered words in English though. Rather than
a single character, we want a sequence of characters. Not surprisingly, Haskell has a data types to
build sequences. The most commonly used sequence is the list. You can have lists of many things:
lists of groceries, lists of planets, but also lists of characters. We can make a literal list in Haskell by
enumerating its elements, separated by commas and surrounded by square brackets. For instance, the
list 1, 2, 3, 4, 5 is written as [1, 2, 3, 4, 5]. Let's try to make a list of characters:
Prelude> ['h','e','l','l','o']
Now we are getting somewhere! Let's look at the type of this list:
Prelude> :type ['h','e','l','l','o']
['h','e','l','l','o'] :: [Char]
Its type is [Char], which should be read as 'list of characters'. Such a list of characters is known as
a string Of course, writing down words in this manner is not very convenient. Fortunately, as the
evaluation of the second to last example already suggests, there is a more convenient notation. We can
represent strings by wrapping characters in double quotes:
Prelude> "hello"
Prelude> :type "hello"
"hello" :: [Char]
We will take this opportunity to seriously demolish some words, but all with the noble cause of learning
some commonly-used Haskell list functions. The first function length returns the length of a list:
Prelude> length "hello"
Prelude> length [1,2,3]
To get a better impression of functions, it is often useful to look at its type:
Prelude> :type length
length :: [a] -> Int
That's one heck of a type! Basically, it says 'give me a list of something (denoted by the a between
the list brackets), then I will give you an Int'. In these so-called type signatures, letters that are not
capitalized are generic, meaning that they can be of some unspecified type. That is, [a] is a list with
elements of some type. But: all elements should be of the same type. An Int is an integral number:
a positive or negative whole number.
Two other basic list functions are head and tail. head returns the first element of a list, tail
everything but the first element:
Prelude> head "hello"
Prelude> tail "hello"
The type of head is the following:
Prelude> :type head
head :: [a] -> a
Hey, two a's! Equipped with the knowledge we have, we know that head is a function that takes a
list of something, and gives back something. But there is an additional constraint here: although a is
some type, all a's have to be the same type. So, applying head to a list of numbers gives a number,
applying head to a list of characters gives a character, etc.
In analogy, the type of tail should now be easy to understand:
Prelude> :type tail
tail :: [a] -> [a]
We apply tail to a list of some type, and get back a list with the same type.
Finally, the last function for now is reverse. We have to admit presenting this function with a bit
of joy, since it will allow us to write our first little useful Haskell program. As expected, reverse
reverses the elements of a list:
Prelude> reverse "hello"
Olé! And another one:
Prelude> reverse "level"
Hold on there! We bumped into a palindrome: a word that is read the same way, no matter whether it is
read forward or backward. Now, suppose we would like to write our own function to determine whether
a word is a palindrome. We first need to make a slightly more formal definition of a palindrome: a word
is a palindrome if it is equal to its reverse. In Haskell we can compare values using the == operator:
Prelude> "hello" == "hello"
Prelude> "hello" == "olleh"
Such a comparison evaluates to True if both values are equal, or to False in case they are not. True
and False are the only values of the Bool type. Since reverse also returns a value, nothing holds us
from using it in comparisons:
Prelude> "hello" == reverse "hello"
Prelude> "level" == reverse "level"
The test that we devised for detecting palindromes seems to work. But it is a lot of typing. Luckily,
we can generalize this into a function. Let's replace both words by the symbolic name word (but don't
execute this in ghci yet, since it does not know this symbolic name):
word == reverse word
And as a next step, Let's do some magic:
Prelude> let palindrome word = word == reverse word
This defines the function palindrome taking one argument, and binds this argument to the symbolic
name word. To this function we assign the expression word == reverse word. Play a little with this
function to be convinced that it actually works. Some examples:
Prelude> palindrome "hello"
Prelude> palindrome "level"
Prelude> palindrome "racecar"
If this function is still a mystery to you, it may be useful to write down the application of the function
stepwise for a word that is not a palindrome:
palindrome "hello"
palindrome "hello" = "hello" == reverse "hello"
palindrome "hello" = "hello" == "olleh"
palindrome "hello" = False
and a word that is a palindrome:
palindrome "racecar"
palindrome "racecar" = "racecar" == reverse "racecar"
palindrome "racecar" = "racecar" == "racecar"
palindrome "racecar" = True
Congratulations, you have made your first function, which is in essence a small program!
From words to sentences
So far, we have looked at words in isolation. However, in language, words are often combined to
form a higher level of meaning representation: a sentence. Provided what we have learned about
representing words in Haskell, the step towards representing sentences should be a minor one. We
could, for example, represent sentences in the exactly the same way we represented words:
Prelude> "The cat is on the mat."
"The cat is on the mat."
That's fine for a beginning, although not so convenient. Let us see why. Assume we ask you to give
us the first word of a sentence. In the previous section, we learned that head can be used to get the
first element of a list. Let's try to apply that here:
Prelude> head "The cat is on the mat."
As you probably expected, that didn't work. We represented a sentence as a list of characters (a string),
and hence asking for the first element will give the first character. But wait! What if we represented
a sentence as a list of words?
Prelude> ["The", "cat", "is", "on", "the", "mat", "."]
Prelude> :type ["The", "cat", "is", "on", "the", "mat", "."]
["The", "cat", "is", "on", "the", "mat", "."] :: [[Char]]
Nifty! We just constructed a list, of a list of characters. Though, you may wonder why we made the
punctuation at the end of the sentence a separate "word". Well, this is mostly a pragmatic choice,
because gluing this punctuation sign to mat does not really form a word either. Having the period sign
separate is more practical for future processing. Hence, formally we say that a sentence consists of
tokens, where a token can be a word, a number, and a punctuation sign.
Rinse and repeat:
Prelude> head ["The", "cat", "is", "on", "the", "mat", "."]
Since a word is also a list, we can apply a function to words as well. For example, we can get the first
character of the first word by applying head, to the head of a sentence:
Prelude> head (head ["The", "cat", "is", "on", "the", "mat", "."])
Note that we need parenthesis to force Haskell to evaluate the part in parentheses first. If we do not
enforce this order of evaluation, Haskell will try to evaluate head head first, which makes no sense.
Remember that head requires a list as its argument, and head is not a list.
Now that we know how to represent sentence, this is a good time to try to write yet another small
program. This time, we will write a function to compute the average token length in a corpus (a
collection of texts). Since we did not look at real corpora yet, pick any sentence you like as My Little
Corpus™. The authors will use "Oh, no, flying pink ponies!" The average token length is the sum of
the lengths of all tokens, divided by the total number of tokens in the corpus. So, stepwise, we have to:
1. Get the length of each token in the corpus.
2. Sum the lengths of the tokens.
3. Divide the sum by the length of the corpus.
You know how to get the in characters length of a single token:
Prelude> length "flying"
Since you are lazy, you are not going to apply length to every token in the corpus manually. Instead
we want tell Haskell "Hey Haskell! Please apply this length function to each element of the list." It
turns out that Haskell has a function to do this which is called map. Time to inspect map:
Prelude> :type map
map :: (a -> b) -> [a] -> [b]
And we are in for another surprise. The most surprising element is probably the first element in the
type signature, (a -> b). Also surprising is that we now see three types, (a -> b), [a] and [b]. The latter
is simple: this function takes two arguments, (a -> b) and [a], and returns [b]. (a -> b) as the notation
suggests, is a function taking an a and returning a b. So, map is actually a function that takes a function
as its argument, or in functional programming-speak: a higher order function.
So, map is a function that takes a function that maps from a to b, takes a list of as, and returns a list
of bs. That looks a suspicious lot like what we want! We have a list of tokens represented as strings,
the function length that takes a list and returns its length as an integer, and we want to have a list of
integers representing the lengths. Looks like we have a winner!
Prelude> map length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies","!"]
We have now completed our first step: we have the length of each token in the corpus. Next, we have
to sum the lengths that we have just retrieved. Fortunately, Haskell has a sum function:
Prelude> :type sum
sum :: (Num a) => [a] -> a
This function takes a list of as, and returns an a. But where did the (Num a) => come from? Well,
Num is a so-called typeclass. A type can belong to one or more of such typeclasses. But belonging to
a typeclass does not come without cost. In fact, it requires that certain functions need to be defined
for types that belong to it. For instance, the typeclass Num is a typeclass for numbers, which requires
amongst others, functions that define addition or subtraction. Coming back to the type signature, sum
will sum a list of as, but not just any as, only those that belong to the typeclass Num. And after all,
this makes sense, doesn't it? We cannot sum strings or planets, but we can sum numbers. In fact, we
can only sum numbers.
After this solemn introduction into typeclasses, feel free to take a cup of tea (or coffee), and try step
Prelude> :{
sum (map length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"])
By now, you will probably smell victory. The only step that remains is to divide the sum by the length
of the sentence using the division operator (/):
Prelude> :{
sum (map length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"]) /
length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"]
No instance for (Fractional Int)
arising from a use of `/' at <interactive>:1:0-136
Possible fix: add an instance declaration for (Fractional Int)
In the expression:
sum (map length ["Oh", ",", "no", ",", ....])
/ length ["Oh", ",", "no", ",", ....]
In the definition of `it':
it = sum (map length ["Oh", ",", "no", ....])
/ length ["Oh", ",", "no", ....]
And we have... Failure! I hope you poured yourself a cup of herb tea! (again alternatively: espresso!)
While this is all a bit cryptic, the second line (No instance for (Fractional Int)) gives some idea where
the trouble stems from. Fractional is typeclass for fractional numbers, and Haskell complains that Int
is not defined to be of the typeclass Fractional. This sounds obvious, since an integer is not a fractional
number. In other words, Haskell is trying to tell us that there is an Int in some place where it expected
a type belonging to the typeclass Fractional. Since the division is the only new component, it is the
first suspect of the breakdown:
Prelude> :type (/)
(/) :: (Fractional a) => a -> a -> a
First off, notice that we have put the division operator in parentheses. We have done this because the
division operator is used as a so-called infix function: it is a function that is put between its arguments
(like 1.0 / 2.0). By putting an infix operator in parentheses, you are stating that you would like to use
it as a regular function. This means you can do things like this:
Prelude> (/) 1.0 2.0
Anyway, the verdict of the type signature of (/) is clear, it requires two arguments that belong to the
Fractional typeclass. The sum and length that we calculated clearly do not belong to this typeclass,
since they are of the type Int:
Prelude> :{
sum (map length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"])
sum (map length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"])
:: Int
Prelude> :{
length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"]
length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"]
:: Int
Fortunately, Haskell provides the function fromIntegral that converts an integer to any kind of
number. Add fromIntegral, and you surely do get the average token length of the corpus:
Prelude> :{
(sum (map length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"])) /
fromIntegral (length ["Oh", ",", "no", ",", "flying", ",", "pink", "ponies", "!"])
Well, that was a bumpier ride than you might have expected. Don't worry! During our first forays into
Haskell, we were convinced that we were too stupid for this too (and here we are writing a book).
However, after more practice, you will learn that Haskell is actually a very simple and logical language.
Maybe it will feel more like a victory after generalizing this to a function. You can follow the same
pattern as in the palindrome example: replace the sentence with a symbolic name and transform it
into a function:
Prelude> :{
let averageLength l =
fromIntegral (sum (map length l)) / fromIntegral (length l)
Prelude> :{
averageLength ["Oh", ",", "no", ",", "flying", ",", "pink" ,"ponies", "!"]
Congratulations, you just wrote your second function! But wait, you actually accomplished more than
you may expect. Check the type signature of averageLength.
Prelude> :type averageLength
averageLength :: (Fractional b) => [[a]] -> b
You made your first weird type signature. Show it off to your colleagues, significant other, or dog.
averageLength is a function that takes a list of a list of a, and returns a b that belongs to the Fractional
typeclass. But wait, a can be anything, right? What happens if we apply this function to a list of
Prelude> averageLength [["I", "like", "Haskell", "."],
["Ruby", "rocks", "too", "."],
["Who", "needs", "Java", "?"]]
Woo! That's the average sentence length, expressed in number of words. It turns out that, although we
set out to make a function to calculate the average token length, we wrote a function that calculates
the average length of lists in a list (e.g., characters in words, words in sentences, or sentences in a
text). This happens very often when you write Haskell programs: lots of functions are generic and can
be reused for other tasks.
A note on tokenization
When dealing with real-world text, it is usually not neatly split in sentences and tokens. For example,
consider this book - punctuation is usually glued to words. These processes, sentence splitting and
tokenization may seem trivial, unfortunately they are not. Consider the following sentence:
E.g. Jack doesn't have 19.99 to spend.
If we simply perform sentence splitting on periods (.), we will find four sentences:
1. E.
2. g.
3. Jack doesn't have 19.
4. 99 to spend.
Of course, it is just one sentence. Similar problems arise during punctuation: how do we know that
E.g. and 19.99 should not be split? And how about doesn't, which should probably be split as does n't
or does not? Tokenization can be performed accurately, but it requires techniques that you will see in
later chapters. So don't worry, we will get back to proper tokenization later on. We promise!
Of course, up to the point where we handle tokenization, we need material to work on. To make life
easier for you, the material for the first chapters of the book is pre-tokenized in a plain-text file using
two simple rules:
1. One sentence per line.
2. Tokens are separated by a space.
To convert a text file to a Haskell representation, sentence splitting is a matter of splitting by line, and
tokenization a matter of splitting by space. Have a look at the following example:
Prelude> "This is Jack .\nHe is a Haskeller ."
"This is Jack .\nHe is a Haskeller ."
This is exactly the representation that we will be using for our textual data. As you can see, the tokens
are separated by spaces. Both sentences are separated using a newline. When writing down a string
literally, you can insert a newline using \n.
Haskell provides a lines function to split up a string by line. Not surprisingly, this function accepts
a string as its first argument, and will return a list of strings:
Prelude> :type lines
lines :: String -> [String]
Prelude> lines "This is Jack .\nHe is a Haskeller ."
["This is Jack .","He is a Haskeller ."]
That was easy! Now to the actual tokenization. For all sentences, we have a string representing the
sentence. We want to split this string on the space character. Haskell also has a function to do this,
named words. words is nearly the same function as lines, except that it splits on spaces rather than
Prelude> words "This is Jack ."
That will do, but we have to apply this to every sentence in the list of sentences. Recall that we can
use the map function we have seen earlier to apply the words function to each element of the list of
(untokenized) sentences:
Prelude> map words (lines "This is Jack .\nHe is a Haskeller .")
Allright! That will do the job. We know how to turn this into a full-fledged function:
Prelude> let splitTokenize text = map words (lines text)
Prelude> splitTokenize "This is Jack .\nHe is a Haskeller ."
This is a good moment to beautify this function a bit. To make it simpler, we first need to get rid of
the parentheses. We used the parentheses to tell Haskell that it should evaluate lines text first, because
it would otherwise try to map over the function lines, which would fail, because it is not a list. Very
often, you will encounter function applications of the form f(g(x)), or f(g(h(x))), etc. Haskell provides
the (.) function to combine such function applications. So, f(g(x)) can be rewritten to (f . g) x (apply
function f to the outcome of g(x)) and f(g(h(x))) as (f . g . h) x (apply function f to the outcome of a
function g, which is in turn applied to the outcome of h(x)). As you can see, this so-called function
composition makes things much easier to read. We can now rewrite our tokenization function by using
function composition:
Prelude> let splitTokenize text = (map words . lines) text
This states that we apply map words to the outcome lines text. This may not yet seem so interesting.
However, it allows us to make yet another simplification step. Consider the type of the map function:
Prelude> :type map
map :: (a -> b) -> [a] -> [b]
map takes a function, and a list, and returns a list. Now we will do something that may look weird,
but is very common in functional programming.
Prelude> :type map words
map words :: [String] -> [[String]]
Applying map to just one argument will give... another function! In fact, what we just did is bind only
the first argument of the map function, leaving the second unbound. This gives a novel map function
that only takes one argument, as it has its first argument implicitly bound to the function words.
This process is called currying (indeed, named after the mathematician Haskell Curry) in functional
programming slang.
If we look back at our splitTokenize function, and look up the type of map words . lines, we see
that it is a function that takes a String and returns a list of a list of strings:
Prelude> :type map words . lines
map words . lines :: String -> [[String]]
In our function body, we apply this function to the argument text. Of course, this is not really necessary,
because map words . lines already defines our function (as we have shown above). We just need to
bind this to the name splitTokenize. Consequently the function can once more be simplified:
Prelude> let splitTokenize = map words . lines
splitTokenize :: String -> [[String]]
Prelude> splitTokenize "This is Jack .\nHe is a Haskeller ."
Word lists
In the following two sections, we will introduce two prototypical tasks related to words. The first is
to make a word (or actually token) list, the second task is making a word frequency list.
A word list is a very simple data structure: it is just a list of unique words or tokens that occur in a
text. Our corpus is also just a list of words, but since it contains duplicates, it is not a word list. The
obvious method to make a word list is to go through a corpus word by word, and adding words that
we have not yet seen to a second list. This requires some functions we haven't seen yet:
Adding an element to a list.
Checking whether an element is (or is not) in a list.
Constructing a list while traversing another list.
We like easy things first, so let's start with the first item: adding an element to a list. We have seen
the head function before that chops of the head of the list and returns it. But we can also do the
reverse: take a list and give it a new head. The old head then becomes the head of the tail (are you still
following?). In Haskell, we can do this using the (:) function:
Prelude> 2 : [3,4,5]
Ain't that great? We can also add a head, and yet another:
Prelude> 1 : 2 : [3,4,5]
What if we do not have an element yet? Add the head to the empty list ([]):
Prelude> "Hi" : []
With that covered, the next thing we need to be able to do is checking whether some element belongs
to a list. We can do this using the elem function. It takes an element as its first argument, and a list
as its second. It will return a Bool of the value True if the element was in the list, or False otherwise.
For example:
Prelude> elem 2 [1,2,3,4,5]
Prelude> elem 6 [1,2,3,4,5]
The function notElem is exactly the inverse of elem, and returns True if an element is not in the list,
and False otherwise:
Prelude> notElem "foo" ["foo","bar","baz"]
Prelude> notElem "pony" ["foo","bar","baz"]
Ok, so we want to add an element to a list if, but only if, it is true that it is not yet a member of
that list. Or in other words, the addition is conditional. Haskell provides a set of keywords to model
conditionals, if..then..else. The structure is like this:
if expr then a else b
This whole structure itself is an expression. This expression evaluates to a if expr evaluates to True
or to b if expr evaluates to False. To give a working, but useless example:
Prelude> if 1 == 2 then "cuckoo" else "egg"
Prelude> if 1 == 1 then "cuckoo" else "egg"
This looks exactly like what we need. Just fill in the blanks:
Prelude> if elem "foo" ["foo","bar","baz"] then ["foo","bar","baz"]
else "foo" : ["foo", "bar", "baz"]
Prelude> if elem "pony" ["foo","bar","baz"] then ["foo","bar","baz"]
else "pony" : ["foo", "bar", "baz"]
That's a bit contrived, but (as you hopefully see) not if we rewrite it to a function:
Prelude> let elemOrAdd e l = if elem e l then l else e:l
Prelude> elemOrAdd "foo" ["foo", "bar", "baz"]
Prelude> elemOrAdd "pony" ["foo", "bar", "baz"]
Good, now we need to apply this to all words in a text, starting with an empty list. Haskell provides a
function to do this, but brace yourself, the first time it may look a bit 'difficult'. It is named foldl (a
so-called) left fold. A left fold traverses a list from head to tail, applying a function to each element,
just like map. However, the difference is that it can, but does not necessarily return a list. As such, it is
a generalization of the map function. As usual, you can inspect the type signature to see the arguments
of foldl:
Prelude> :type foldl
foldl :: (a -> b -> a) -> a -> [b] -> a
Now consider this example using foldl:
Prelude> foldl (+) 0 [1,2,3,4,5]
Stepwise, this fold is executed in the following manner:
foldl (+) 0 [1,2,3,4,5]
foldl (+) ((0)+1) [2,3,4,5]
foldl (+) (((0)+1)+2) [3,4,5]
foldl (+) ((((0)+1)+2)+3) [4,5]
foldl (+) (((((0)+1)+2)+3)+4) [5]
foldl (+) ((((((0)+1)+2)+3)+4)+5)) []
So, it works by applying a function to some initial argument (0 in this case) as its first argument,
and the first element of the list as its second argument. When processing the second element of the
list, this expression is then the first argument of the function, and the second element is the second
argument, etc. The first argument of the function that is applied is also called the accumulator, since
it accumulates results up till that point.
This could also work for our elemOrAdd function. Unfortunately, elemOrAdd requires the
accumulator as the second argument, and the function passed to foldl as the first argument. Compare
the type signatures:
Prelude> :type foldl
foldl :: (a -> b -> a) -> a -> [b] -> a
Prelude> :type elemOrAdd
elemOrAdd :: (Eq a) => a -> [a] -> [a]
In the function that is the first argument to foldl, the return type is the same as the type of the first
argument. In the case of elemOrAdd, the type of the second argument corresponds to that of the first.
Of course, an easy 'hack' to solve this, is to redefine elemOrAdd, switching its arguments, and plug
it into foldl:
Prelude> let elemOrAdd l e = if elem e l then l else e:l
Now, since we are building a list, we use the empty list ([]) as the initial accumulator for this fold:
Prelude> foldl elemOrAdd [] ["blue", "blue", "red", "blue", "red"]
That looks good! Stepwise, the fold works like this:
foldl elemOrAdd [] ["blue", "blue", "red", "blue", "red"]
foldl elemOrAdd ("blue":([])) ["blue", "blue", "red", "blue", "red"]
foldl elemOrAdd ("blue":([])) ["blue", "red", "blue", "red"]
foldl elemOrAdd ("blue":([])) ["red", "blue", "red"]
foldl elemOrAdd ("red":("blue":([]))) ["blue", "red"]
foldl elemOrAdd ("red":("blue":([]))) ["red"]
foldl elemOrAdd ("red":("blue":([]))) []
Now we wrap it up in another function, and you have constructed two functions that, together, make
word lists:
Prelude> let wordList = foldl elemOrAdd []
Prelude> wordList ["blue", "blue", "red", "blue", "red"]
While our little word list function works fine on small texts, it will not be very efficient for big corpora.
The reason is simple - suppose that we have already found 100,000 different tokens. For every word,
it would have to check the list of 100,000 tokens. There is no other way of doing this than to traverse
the list word by word. Or, on average, we compare a token to 100,000 / 2 = 50,000 elements in the
list. As a computer scientist would say: elemOrAdd works in linear time, its processing time is linear
to the number of different tokens that were seen.
This is a typical case of picking the wrong data structure for the task. But for illustrative purposes,
using lists was nice and simple. However, since you are a working programmer, you want workable
solutions. Bring in the sets! A set is, like the mathematical set, a collection that does not contain
duplicate elements. That's good, because a word list does not contain duplicate elements. Silly us,
we were fooled by the word list. What we actually want to build is a word set. In fact, it is just for
historical purposes that it is called a word list.
Beside the uniqueness of elements, another nice property of sets, as they are normally implemented, is
that set membership can be checked rather quickly. In the sets that we will use, membership checking
is in logarithmic time. Or in other words, if comparison took one second, we would on average need
50,000 seconds to search the list mentioned earlier, but only log(100,000) or approximately 11.5
seconds to check whether the element is in a set. Talking about optimizations!
Haskell provides set functionality, but not in the so-called Prelude. Prelude is a module that contains
functions. The Prelude module is always loaded, so its functions are always available (unless you
explicitly ask Haskell to hide them). The functions map, head, tail, and length, for instance, are
defined in the Prelude. Functions for set manipulation, on the other hand, are defined in a module
named Data.Set. For the time being, we will access functions from modules by prefixing the name of
the module. For instance, this will give us the empty set:
Prelude> Data.Set.empty
fromList []
Like a list, a set can contain elements of various types. We see this when inspecting the type signature
of the empty function:
Prelude> :type Data.Set.empty
Data.Set.empty :: Data.Set.Set a
empty returns a Set of some type a. We can also construct a Set from a list using the fromList
Prelude> Data.Set.fromList [5,2,5,8,1,1,23]
fromList [1,2,5,8,23]
As you can see here, the set does not contain duplicates. Another nice property of Haskell sets is that
they are ordered. We can also do the inverse, convert a set to a list using toList:
Prelude> Data.Set.toList (Data.Set.fromList [5,2,5,8,1,1,23])
Elements can be added to or removed from a Set using respectively the insert and delete functions.
Both functions return a set with that element inserted or removed:
Prelude> Data.Set.insert 42 (Data.Set.fromList [5,2,5,8,1,1,23])
fromList [1,2,5,8,23,42]
Prelude> Data.Set.delete 5 (Data.Set.fromList [5,2,5,8,1,1,23])
fromList [1,2,8,23]
Finally, we can check whether some value is a member of a set by using the member function:
Prelude> Data.Set.member 23 (Data.Set.fromList [5,2,5,8,1,1,23])
Prelude> Data.Set.member 24 (Data.Set.fromList [5,2,5,8,1,1,23])
We have now seen enough to change our word list function. Rather than checking whether a value is
in a list and adding it if not, we check whether it is in a Set and add it in when it is not:
Prelude> :{
let elemOrAdd s e =
if Data.Set.member e s then s else Data.Set.insert e s
Prelude> elemOrAdd (Data.Set.fromList [5,2,5,8,1,1,23]) 24
fromList [1,2,5,8,23,24]
That was simple. But it feels a weird, right? The most vital characteristic of a set is that it never contains
duplicate elements, why do we need to check for duplicates? We don't. So, forget about elemOrAdd,
we will only use Data.Set.insert from this point. Our objective now is to traverse a list of tokens,
adding each token to a set, starting with the empty set. Our first take is this:
Prelude> let wordSet = foldl Data.Set.insert Data.Set.empty
However, this will not work. Remember that in the function we give to foldl, the accumulator has to
be the first argument (the reason why we swapped the elements of our initial elemOrAdd function)?
We are accumulating a Set, but the set is the second argument to Data.Set.insert. We will pull a little
trick out of our hat.
Prelude> let wordSet = foldl (\s e -> Data.Set.insert e s) Data.Set.empty
You might be thinking "Oh, no, more syntax terror! Does it ever stop?" Actually, ( e -> Data.Set.insert
e s) is very familiar. You could see it as an inline function. In functional programming jargon, this is
called a lambda. Check out the type signature of the lambda:
Prelude> :type (\s e -> Data.Set.insert e s)
(\s e -> Data.Set.insert e s)
:: (Ord a) => Data.Set.Set a -> a -> Data.Set.Set a
It is just a function, it takes a set of some type a, a value of type a, and returns a. Additionally, a has
the typeclass Ord, which means that some comparison operators should be defined for a. The lambda
has two arguments that are bound to s and e. The function body comes after the arrow. To emphasize
that this is just a function, the following functions are equivalent:
myFun = (\s e -> Data.Set.insert e s)
myFun s e = Data.Set.insert e ss
Back to our wordSet function. We used the lambda to swap the arguments of Data.Set.insert.
Data.Set.insert takes a value and a set, our lambda takes a set and a value. The rest of the function
follows the same pattern as wordList, except that we start with an empty set rather than an empty list.
The function works as expected:
Prelude> wordSet ["blue", "blue", "red", "blue", "red"]
fromList ["blue","red"]
You have done it! You are now not only able to make a function that creates a word list, but also one
that is performant.
1. To measure the vocabulary of a writer, a so-called type-token ratio can be calculated. This is the
number of distinct tokens occurring in a text (types) divided by the total number of tokens in that
Equation 2.1. Type-token ratio
For instance the phrase "to be or not to be" contains six tokens and four types (to, be, or, not). The
type-token ratio of this phrase is 4 / 6 = 2 / 3.
Write a function that calculates the type-token ratio of a list of tokens. You can use the Data.Set.size
function to get the number of elements in a set.
Storing functions in a file
Now that we are writing longer and longer functions, it becomes more convenient to define functions
in a file rather than the ghci prompt. You can do this by creating a file using a plain-text editor with the
.hs extension. Functions can be written down in the same manner as in ghci, but without the preceding
let keyword. It is also highly recommended to add a type signature before the function. Haskell will
check the function against the type signature, and report an error if they do not correspond. This will
help you catch incorrect function definitions.
The palindrome function discussed earlier in this chapter can be written to a file like this:
palindrome :: (Eq a) => [a] -> Bool
palindrome word = word == reverse word
If you saved this file as example.hs, you can load it in ghci using the :l (shorthand for :load) command:
Prelude> :l example
[1 of 1] Compiling Main ( example.hs, interpreted )
Ok, modules loaded: Main.
*Main> palindrome "racecar"
For code fragments that use a module other than the prelude, add an import statement at the top of
the file. For example, the wordSet function from the previous section should be saved to a text file
in the following manner:
import qualified Data.Set
wordSet :: Ord a => [a] -> Data.Set.Set a
wordSet = foldl (\s e -> Data.Set.insert e s) Data.Set.empty
From now on, we assume that examples are written to a text file, except when the Prelude> occurs
in the example.
Word frequency lists
The word list function that we built in the previous section works is useful for various tasks, like
calculating the type-token ratio for a text. For some other tasks this is not good enough - we want to
be able to find out how often a word was used. We can expand a word list with frequencies to make
a word frequency list.
To be able to store word frequencies, every word has to be associated with an integer. We could store
such an association as a tuple. A tuple is a data type with a fixed number of elements and a fixed type
for an element. Examples of tuples are:
• (1,2,3)
• ("hello","world")
• ("hello",1)
As you can see, they differ from lists in that they can have values of different types as elements.
However, if you inspect the type signatures of these tuples, you will see that the length and type for
each position is fixed:
Prelude> :type (1,2,3)
(1,2,3) :: (Num t, Num t1, Num t2) => (t, t1, t2)
Prelude> :type ("hello","world")
("hello","world") :: ([Char], [Char])
Prelude> :type ("hello",1)
("hello",1) :: (Num t) => ([Char], t)
To store frequencies, we could use a list of tuples of the type [([Char], Int)]. The phrase "to be or
not to be" could be stored as
However, this would be even less efficient than using lists for constructing word lists. First, like
elemOrAdd we would potentially have to search the complete list to locate a word. Second, we would
have to reconstruct the list up to the point of the element. In the elemOrAdd function we could just give
the list a new head, but now we would have to replace the element to update the word frequency and
add all preceding list items again. Since Haskell is a 'pure' language, we cannot modify existing values.
A more appropriate data type for this task is a map (not to be confused with the map function). A
map maps a key to a value. In Haskell, maps are provided in the Data.Map module. Like sets, we
can make an empty map:
Prelude> Data.Map.empty
fromList []
When you inspect the type signature of the empty map, you can see that it parametrizes over two types,
a type for the key and a type for values:
Prelude> :type Data.Map.empty
Data.Map.empty :: Data.Map.Map k a
We can construct a Map from a list of binary tuples (tuples with two elements), where the first element
of the tuple becomes the key, and the second the value:
Prelude> Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)]
fromList [("be",2),("not",1),("or",1),("to",2)]
Prelude> :type Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)]
Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)]
:: (Num t) => Data.Map.Map [Char] t
This also binds the types for the map: we are mapping from keys of type string to values of type t
that belongs to the t typeclass. No specific value for types is used (yet), because the numbers could
be integers or fractionals.
The insert function is used to add a new mapping to the Map:
Prelude> :{
Data.Map.insert "hello" 1
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
fromList [("be",2),("hello",1),("not",1),("or",1),("to",2)]
If a mapping with the given key already exists, the existing mapping is replaced:
Prelude> :{
Data.Map.insert "be" 1
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
fromList [("be",1),("not",1),("or",1),("to",2)]
Looking up values is a bit peculiar. You can lookup a value with the lookup function. However, if
you inspect the type signature, you will see that the value is not returned as is:
Prelude> :type Data.Map.lookup
Data.Map.lookup :: (Ord k) => k -> Data.Map.Map k a -> Maybe a
Rather than returning a value, it returns the value packed in some box called Maybe. Maybe a is a type
that has just two possible so-called constructors, Just a or Nothing. You can put your own values in
a Maybe box using the Just a constructor:
Prelude> Just 22
Just 22
Prelude> :type Just 22
Just 22 :: (Num t) => Maybe t
Prelude> Just [1,2,3,4,5]
Just [1,2,3,4,5]
Prelude> Just "stay calm"
Just "stay calm"
Prelude> :type Just "stay calm"
Just "stay calm" :: Maybe [Char]
You can also make a box that contains vast emptiness with the Nothing constructor:
Prelude> Nothing
Prelude> :type Nothing
Nothing :: Maybe a
These boxes turn out to be pretty cool: you can use them to return something or nothing from functions,
without resorting to all kinds of abominations as exceptions or null pointers (if you never heard of
exceptions or pointers, do not worry, you have a life full of bliss). Since Maybe is so nice, the lookup
function uses it. It will return the value packed with in a Just constructor if the key occurred in the
map, or Nothing otherwise:
Prelude> :{
Data.Map.lookup "to"
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
Just 2
Prelude> :{
Data.Map.lookup "wrong"
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
As for handling these values - we will come to that later. Mappings are deleted from a Map by key
with the delete function. If a key did not occur in the Map, the original map is returned:
Prelude> :{
Data.Map.delete "to"
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
fromList [("be",2),("not",1),("or",1)]
Prelude> :{
Data.Map.delete "wrong"
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
fromList [("be",2),("not",1),("or",1),("to",2)]
Finally, a Map can be converted to a list using the toList function:
Prelude> :{
(Data.Map.fromList [("to",2),("be",2),("or",1),("not",1)])
Alright. Back to our task at hand: constructing a word frequency list. As with word lists, we want to
traverse a list of words, accumulating data. So, the use of foldl is appropriate for this task. During
each folding step, we take the Map created in a previous step. We then lookup the value for the current
step in the Map. If it does not exist, we add it to the Map giving it a frequency of one. Otherwise, we
want to increase the frequency by one. The countElem function does this:
import qualified Data.Map
countElem :: (Ord k) => Data.Map.Map k Int -> k -> Data.Map.Map k Int
countElem m e = case (Data.Map.lookup e m) of
Just v -> Data.Map.insert e (v + 1) m
Nothing -> Data.Map.insert e 1 m
This function introduces the case construct. Remember that lookup uses the nifty Maybe data type?
The case construct allows us to select an expression based on a constructor. If lookup returned a
value using the Just constructor, the key was in the Map. In this case, we bind the value to the name
v and add a new value for this key. This value is the old value for this key incremented by one. If a
value with the Nothing constructor was returned, the key was not in the Map. So, we will add it, and
give it a (frequency) value of 1.
The countElem function works as intended:
*Main> foldl countElem Data.Map.empty ["to","be","or","not","to","be"]
fromList [("be",2),("not",1),("or",1),("to",2)]
While this was a nice exercise, the Data.Map.insertWith function can drastically shorten our function.
This function uses an update function to update a value, or a specified value if the key is not present
in the Map:
*Main> :t Data.Map.insertWith
:: Ord k =>
(a -> a -> a) -> k -> a -> Data.Map.Map k a -> Data.Map.Map k a
The update function gets the specified value as its first argument, and the old value as its second
argument. Using insertWith, we can shorten our function to:
countElem :: (Ord k) => Data.Map.Map k Int -> k -> Data.Map.Map k Int
countElem m e = Data.Map.insertWith (\n o -> n + o) e 1 m
If an element was not seen in the Map yet, a frequency of 1 will be inserted. If the element does occur
as a key in the map, the lambda adds one to the old frequency. With countElem in our reach, we can
define the freqList function:
freqList :: (Ord k) => [k] -> Data.Map.Map k Int
freqList = foldl countElem Data.Map.empty
In the next section you will see how to read real text corpora using the so-called IO monad. Before
diving into the IO monad, we will give a short introduction to monads. In Haskell, it happens very
often that you want to perform a series of computations on values that are wrapped using some type,
such as Maybe or a list. For instance, suppose that you have a Map that maps a customer name to a
customer number, and yet another Map that maps a customer number to a list of order numbers:
*Main> let customers = Data.Map.fromList [("Daniel de Kok", 1000),
("Harm Brouwer", 1001)]
*Main> let orders = Data.Map.fromList [(1001, [128])]
Prelude> Data.Map.lookup "Harm Brouwer" customers
Just 1001
Prelude> Data.Map.lookup 1001 orders
Just [128]
Now, we want to write a function that extracts a list orders for a given customer, wrapped in a Maybe,
so that Nothing can be returned if the customer is not in the list or if the customer had no orders. Your
first attempt will probably be along the following lines:
lookupOrder0 :: Data.Map.Map String Integer ->
Data.Map.Map Integer [Integer] ->
String -> Maybe [Integer]
lookupOrder0 customers orders customer =
case Data.Map.lookup customer customers of
Nothing -> Nothing
Just customerId -> Data.Map.lookup customerId orders
This function works as intended:
*Main> lookupOrder0 customers orders "Jack Sparrow"
*Main> lookupOrder0 customers orders "Daniel de Kok"
*Main> lookupOrder0 customers orders "Harm Brouwer"
Just [128]
But case constructs will quickly start to stack up when more functions are called that return Maybe.
For each Maybe we follow the same procedure: if the value is Nothing we end the computation with
that value, if the value is Just x we call the next function that possibly uses x as an argument. This
is where the Monad typeclass kicks in: all data types that are of the Monad typeclass implement
the (>>=) function. This function joins computations resulting in that data type according to some
logic. For instance, the Maybe monad combines expressions that return Maybe in such a way that if
one expression returns Nothing, the whole joined expression also returns Nothing, just like our case
construct in the example above. Consequently, we could rewrite the example above as:
lookupOrder1 :: Data.Map.Map String Integer ->
Data.Map.Map Integer [Integer] ->
String -> Maybe [Integer]
lookupOrder1 customers orders customer =
Data.Map.lookup customer customers >>= (\m -> Data.Map.lookup m orders)
That surely shortened the function! But what does it do? Well, the function performs the following
Data.Map.lookup customer customers: Lookup customer in customers.
(>>=): If the expression on the left-hand returned Nothing, the value of the full expression, and
consequently lookupOrder1, is Nothing. If the lookup returned a value of type Just Integer, extract
the Integer from the Just constructor, and pass it as the argument of the next function.
(\m -> Data.Map.lookup m orders): Lookup the supplied argument in the orders Map.
The result becomes the result of lookupOrder1. We had to use a lambda, to make the element to
be looked up the first argument.
The type signature of (>>=) is also very illustrative:
*Main> :type (>>=)
(>>=) :: (Monad m) => m a -> (a -> m b) -> m b
The (>>=) simply unwraps a value from the monad and feeds it to a function that returns a value
wrapped in the same monad. The (>>=) function has the freedom to perform any operation (including
not calling (a -> mb)), as long as it returns a value of type m b.
The actual implementation of (>>=) for the Maybe monad is very simple:
(Just x) >>= k = k x
Nothing >>= _ = Nothing
That's all! If the left-hand side expression evaluates to Just x, the expression on the left hand side is
evaluated with x as its argument. If the left-hand side evaluates to Nothing, the right-end side is not
evaluated, and the whole expression returns Nothing.
There is yet another function that is essential to monads named return, it does nothing else than
wrapping a value in that monad. For instance, the maybeBool function wraps a Bool value in a Maybe
maybeBool :: Bool -> Maybe Bool
maybeBool = return
This is maybeBool in action:
*Main> maybeBool True
Just True
*Main> maybeBool False
Just False
The implementation of return for the Maybe monad is trivial:
return = Just
Let's get back to our order lookup function. You may have noticed that the (>>=) function is somewhat
imperative in nature: it chains a set of expressions, where the left-hand expression is evaluated before
the right-hand. Due to this nature, Haskell has a do notation that resembles imperative programs. This
is lookupOrder1 using the do-notation:
lookupOrder2 :: Data.Map.Map String Integer ->
Data.Map.Map Integer [Integer] ->
String -> Maybe [Integer]
lookupOrder2 customers orders customer = do
customerId <- Data.Map.lookup customer customers
orders <- Data.Map.lookup customerId orders
return orders
You can see that we added the do keyword to start the do-notation. Every expression that follows can
be seen as just one element in a sequence of (>>=) expressions. Consequently, each line is governed by
the 'laws' of the monad. If the first lookup fails, no further evaluation is performed, and lookupOrder2
will return Nothing. Otherwise, computation continues. The do-notation also allows the use of the
backward arrow (<-) this arrow extracts a value from the monad, and binds it to a variable.
We can simplify lookupOrder2 further. The last lookup already returns a value wrapped in the Maybe
monad, there is no need to extract it using <- and wrap it again with return:
lookupOrder :: Data.Map.Map String Integer ->
Data.Map.Map Integer [Integer] ->
String -> Maybe [Integer]
lookupOrder customers orders customer = do
customerId <- Data.Map.lookup customer customers
Data.Map.lookup customerId orders
Reading a text corpus
Up to this point we have been using very artificial text corpora. At most a few sentences. But you are
in it for the real deal, right? Lucky you, we will use a real (like really real) corpus starting from this
very moment. Of course, just to test functions we will start with small examples. But you will be able
to apply your functions to real data.
So-called I/O (input/output) is a delicate matter in Haskell. The reason being that, as a pure functional
language, it is not possible to modify expressions or values once they exist. This leads to an admirable
quality of Haskell: given the same input, a function will always return the same output. Or in other
words, a function does not have side-effects. Unlike most other languages, there is no state in a function
that can change, so the output can also not change. If a function always evaluates to the same value
given the same input, how can you have I/O? For example, suppose that we open a file, and use a
function read a byte. And then we read yet another byte. The second byte may be a different one. The
reading function has a side-effect: it increases the position within the file.
The Haskell developers have, clever as they are, found a solution to get Pandora's box into Haskell.
I/O in Haskell is performed in the so-called IO monad. The IO monad is not very different from the
Maybe monad, it implements the (>>=) and return functions as required for monads. However, there
is a subtle, but very important difference. Remember that you can extract a value from a Maybe value
using its Just constructor?
value = case someMaybe of
Just x = x
The IO monad does not have a public constructor, so there is no way to pry a value out of an IO monad.
If you read a file as a list of Char, it resides in the IO monad. You can apply any list function to this
list, however, the result of the function that is applied to a list will also have to reside in the IO monad.
A value can never escape the IO monad. And this is how impure I/O is possible in Haskell without
sacrificing purity of the language: impure data stays in the IO monad, and can never escape.
Now on to some real work. As said, functions that do IO return a value wrapped in IO. For instance,
the putStrLn function returns an empty tuple packed in the IO monad:
Prelude> :type putStrLn "hello world!"
putStrLn "hello world!" :: IO ()
This is just a normal value, you can bind it to a name in ghci:
Prelude> let v = putStrLn "hello world!"
v :: IO ()
However, if we evaluate the value in ghci, it will execute this I/O action:
Prelude> v
hello world!
Of course, the same thing happens if we evaluate putStrLn directly:
Prelude> putStrLn "hello world!"
hello world!
Now, let's get to the interesting part: reading a file. In the files distributed with this book, you will find
the file brown.txt. This file contains the Brown corpus, a corpus of written text of various kinds. The
Brown corpus is already tokenized, which makes our life a bit easier. Ok, first we need to open the file
using the IO.openFile function. openFile requires a filename and an I/O mode as its arguments,
and it returns a handle packed in the IOmonad:
Prelude> :type IO.openFile
:: FilePath
-> GHC.IO.IOMode.IOMode
-> IO GHC.IO.Handle.Types.Handle
We use IO.ReadMode to open brown.txt for reading:
Prelude> let h = IO.openFile "brown.txt" IO.ReadMode
Prelude> :type h
h :: IO GHC.IO.Handle.Types.Handle
The handle is bound to h, but still in the IO monad. It would be nicer if we can access that handle
directly, but we told you that a value can never escape its monad. Surprisingly, we can extract the
value from the IO monad in ghci. The reason that you can is that ghci lives in the IO monad itself. So,
the value will still never leave IO. We can bind the value to a name (or pattern) using <-:
Prelude> h <- IO.openFile "brown.txt" IO.ReadMode
Prelude> :type h
h :: GHC.IO.Handle.Types.Handle
That gives us the handle, bound to h. The next function that we will use is IO.hGetContents, which
returns unread data as a String wrapped in the IO monad:
Prelude> :type IO.hGetContents
IO.hGetContents :: GHC.IO.Handle.Types.Handle -> IO String
As we mentioned earlier, Haskell is a lazy language: expressions are only evaluated when necessary.
The same thing applies to I/O: the relevant contents of a file are only read once you start extracting
characters from the String. With some smart programming, it is not necessary for your program to
read the whole file into memory, it will allocate and deallocate chunks of the file as they are used.
Now, get the contents of the file:
Prelude> c <- IO.hGetContents h
Prelude> :type c
c :: String
We can apply the usual list functions to this String:
Prelude> head c
Prelude> length c
Since the file is sentence-splitted using newlines and tokenized using spaces, you can use the lines
and words functions to apply sentence splitting and tokenization. For instance, the first word of the
corpus is:
Prelude> head (words (head (lines c)))
The frequency of the word the is nicely wrapped in a Just constructor:
Prelude> Data.Map.lookup "the" (freqList (words c))
Just 62713
Congratulations! This was your first venture into the world of corpus statistics!
Todo: Zipfian distribution.
Chapter 3. N-grams
In the previous chapter, we have looked at words, and the combination of words into a higher level
of meaning representation: a sentence. As you might recall being told by your high school grammar
teacher, not every random combination of words forms an grammatically acceptable sentence:
Colorless green ideas sleep furiously
Furiously sleep ideas green colorless
Ideas furiously colorless sleep green
The sentence Colorless green ideas sleep furiously (made famous by the linguist Noam Chomsky),
for instance, is grammatically perfectly acceptable, but of course entirely nonsensical (unless you
ate wrong/weird mushrooms, that is). If you compare this sentence to the other two sentences, this
grammaticality becomes evident. The sentence Furiously sleep ideas green colorless is grammatically
unacceptable, and so is Ideas furiously colorless sleep green: these sentences do not play by the rules
of the English language. In other words, the fact that languages have rules constraints the way in which
words can be combined into an acceptable sentences.
Hey! That sounds good for us NLP programmers (we can almost hear you think), language plays by
rules, computers work with rules, well, we’re done, aren’t we? We’ll infer a set of rules, and there!
we have ourselves language model. A model that describes how a language, say English, works and
behaves. Well, not so fast buster! Although we will certainly discuss our share of such rule-based
language models later on (in the chapter about parsing), the fact is that nature is simply not so simple.
The rules by which a language plays are very complex, and no full set of rules to describe a language
has ever been proposed. Bummer, isn’t it? Lucky for us, there are simpler ways to obtain a language
model, namely by exploiting the observation that words do not combine in a random order. That is,
we can learn a lot from a word and its neighbors. Language models that exploit the ordering of words,
are called n-gram language models, in which the n represents any integer greater than zero.
N-gram models can be imagined as placing a small window over a sentence or a text, in which only n
words are visible at the same time. The simplest n-gram model is therefore a so-called unigram model.
This is a model in which we only look at one word at a time. The sentence Colorless green ideas sleep
furiously, for instance, contains five unigrams: “colorless”, “green”, “ideas”, “sleep”, and “furiously”.
Of course, this is not very informative, as these are just the words that form the sentence. In fact, N-
grams start to become interesting when n is two (a bigram) or greater. Let us start with bigrams.
An unigram can be thought of as a window placed over a text, such that we only look at one word at a
time. In similar fashion, a bigram can be thought of as a window that shows two words at a time. The
sentence Colorless green ideas sleep furiously contains four bigrams:
Colorless, green
green, ideas
ideas, sleep
sleep, furiously
To stick to our ‘window’ analogy, we could say that all bigrams of a sentence can be found by placing a
window on its first two words, and by moving this window to the right one word at a time in a stepwise
manner. We then repeat this procedure, until the window covers the last two words of a sentence. In
fact, the same holds for unigrams and N-grams with n greater than two. So, say we have a body of text
represented as a list of words or tokens (whatever you prefer). For the sake of legacy, we will stick to
a list of tokens representing the sentence Colorless green ideas sleep furiously:
Prelude> ["Colorless", "green", "ideas", "sleep", "furiously"]
Hey! That looks like… indeed, that looks like a list of unigrams! Well, that was convenient.
Unfortunately, things do not remain so simple if we move from unigrams to bigrams or some-very-
large-n-grams. Bigrams and n-grams require us to construct 'windows' that cover more than one word
at a time. In case of bigrams, for instance, this means that we would like to obtain a list of lists of
two words (bigrams). Represented in such a way, the list of bigrams in the sentence Colorless green
ideas sleep furiously would look like this:
To arrive at such a list, we could start out with a list of words (yes indeed, the unigrams), and complete
the following sequence of steps:
1. Place a window on the first bigram, and add it to our bigram list
2. Move the window one word to the right
3. Repeat from the first step, until the last bigram is stored
Provided these steps, we first need a way to place a window on the first bigram, that is, we need to
isolate the first two items of the list of words. In its prelude, Haskell defines a function named take
that seems to suit our needs:
Prelude> :type take
take :: Int -> [a] -> [a]
This function takes an Integer denoting n number of elements, and a list of some type a. Given these
arguments, it returns the first n elements of a list of as. Thus, passing it the number two and a list of
words should give us... our first bigram:
Prelude> take 2 ["Colorless", "green", "ideas", "sleep", "furiously"]
Great! That worked out nice! Now from here on, the idea is to add this bigram to a list, and to move
the window one word to the right, so that we obtain the second bigram. Let us first turn to the latter
(as we will get the list part for free later on). How do we move the window one word to the right?
That is, how do we extract the second and third word in the list, instead of the first and second? A
possible would be to use Haskell's !! operator:
Prelude> :t (!!)
(!!) :: [a] -> Int -> a
This operator takes a list of as, and returns the nth element;
Prelude> ["Colorless", "green", "ideas", "sleep", "furiously"] !! 1
Prelude> ["Colorless", "green", "ideas", "sleep", "furiously"] !! 2
Great, this gives us the two words that make up the second bigram. Now all we have to do is stuff
them together in a list:
Prelude> ["Colorless", "green", "ideas", "sleep", "furiously"] !! 1 :
["Colorless", "green", "ideas", "sleep", "furiously"] !! 2 : []
Well, this does the trick. However, it is not very convenient to wrap this up in a function, and moreover,
this approach is not very Haskellish. In fact, there is a better and more elegant solution, namely to
move the list instead of the window. Wait! What? Yes, move the list instead of the window. But how?
Well, we could just look at the first and second word in the list again, after getting rid of the (previous)
first word. In other words, we could look at the first two words of the tail of the list of words:
Prelude> take 2 (tail ["Colorless", "green", "ideas", "sleep", "furiously"])
Now that looks Haskellish! What about the next bigram? and the one after that? Well, we could apply
the same trick over and over again. We can look at the first two words of the tail of the tails of the
list of words:
Prelude> take 2 (tail (tail ["Colorless", "green", "ideas", "sleep", "furiously"]))
... and the tail of the tail of the tail of the list of words:
Prelude> take 2 (tail (tail (tail ["Colorless", "green", "ideas", "sleep", "furiously"])))
In fact, that last step already gives us the last bigrams in the sentence Colorless green ideas sleep
furiously. The last step would be to throw all these two word lists in a larger list, and we have ourselves
a list of bigrams. However, whereas this is manageable by hand for this particular example, think
about obtaining all the bigrams in the Brown corpus in this manner (gives you nightmares, doesn't it?).
Indeed, we would rather like to wrap this approach up in a function that does all the hard word for us.
Provided a list, this function should take its first two arguments, and then repetitively do this for the
tail of this, and the tail of the tail of this list, and so forth. In other words, it should simply constantly
take the first bigram of a list, and do the same for its tail:
bigram :: [a] -> [[a]]
bigram xs = take 2 xs : bigram (tail xs)
Wow! That almost looks like black magic, doesn't it? The type signature reveals that the function
bigram takes a list of as, and returns a list of list of as. The latter could be a list of bigrams, so this looks
promising. The function takes the first two elements of the list of as, and places them in front of the
result of applying the same function to the tail of the list of as. Eehh.. what? Congratulations! You have
just seen your first share of recursion magic (or madness). A recursive function is a function that calls
itself, and whereas it might look dazzling on first sight, this function actually does nothing more than
what we have done by hand in the above. It collects the first two elements of a list, and then does the
same for the tail of this list. Moreover, it stuffs the two word lists in a larger list on the fly (we told you
the list stuff would come in for free, didn't we?). But wait, will this work? Well, let us put it to a test:
Prelude> bigram ["Colorless", "green", "ideas", "sleep", "furiously"]
["furiously"],[],*** Exception: Prelude.tail: empty list
And the answer is... almost! The function gives us the four bigrams, but it seems to be too greedy:
it does not stop looking for bigrams after collecting the last bigram in the list of words. But did we
tell it when to stop then? Nope, we didn't. In fact, we have only specified a so-called recursive step of
our recursive function. What we miss is what is called a stop condition (also known as a base case).
In a recursive definition, a stop condition defines when a function should stop calling itself, that is,
when our recursive problem is solved. In absence of a stop condition, a recursive function will keep
calling itself for eternity. In fact, this explains above the error, we didn't specify a stop condition so the
function will keep looking for bigrams for eternity. However, as the list of words is finite, the function
will run into trouble when trying to look for bigrams in the tail of an empty list, and this is exactly
what the exception tells us. So, how to fix it? Well, add a stop condition that specifies that we should
stop looking for bigrams when the tail of a list contains only one item (as it is difficult to construct a
bigram out of only one word). We could do this using an if..then..else structure:
bigram :: [a] -> [[a]]
bigram xs = if length(xs) >= 2
then take 2 xs : bigram (tail xs)
else []
This should solve our problems:
Prelude> bigram ["Colorless", "green", "ideas", "sleep", "furiously"]
And, indeed it does. When there is only one word left in our list of words, the bigram function returns
an empty list, and moreover, it will stop calling itself therewith ending the recursion. So, lets see how
this works with an artificial example. First we will recursively apply the bigram function until it is
applied to a list that has less than two elements:
Prelude> bigram [1,2,3,4]
bigram [1,2,3,4] = [1,2] : bigram (tail [1,2,3,4])
bigram [2,3,4] = [2,3] : bigram (tail [2,3,4])
bigram [3,4] = [3,4] : bigram (tail [3,4])
bigram [4] = []
Application of the bigram function to a list with less than two elements results in an empty list.
Moreover, the bigram function will not be applied recursively again as we have reached our stop
condition. Now, the only thing that remains is to unwind the recursion. That is, we have called the
bigram function from within itself for three times, and as we have just found the result to its third and
last self call, we can now reversely construct the result of the outermost function call:
bigram [3,4] = [3,4] : []
bigram [2,3,4] = [2,3] : [3,4] : []
bigram [1,2,3,4] = [1,2] : [2,3] : [3,4] : []
Great! Are you still with us? As L. Peter Deutsch put it: "to iterate is human, to recurse divine."
Whereas recursive definitions may seem difficult on first sight, you will find they are very powerful
once you get the hang of them. In fact, they are very common in Haskell, and this will certainly be the
first of many to come in the course of this book. Lets stick to the bigram function a little longer, because
whereas the above works, it is aesthetically unpleasing. That is, we used an if..then..else structure to
define our stop condition, but Haskell provides a more elegant way to do this through so-called pattern
matching. Pattern matching can be thought of as defining multiple definition of the same functions,
each tailored and honed for a specific argument pattern. Provided an argument, Haskell will then pick
the first matching definition of a function, and return the result its application. Hence, we can define
patterns for the stop condition and recursive step as follows:
bigram :: [a] -> [[a]]
bigram [x] = []
bigram xs = take 2 xs : bigram (tail xs)
The second line represents the stop condition, and the third the familiar recursive step. Provided the list
of words in the sentence Colorless green ideas sleep furiously, Haskell will match this to the recursive
step, and apply this definition of the function to the list. When the recursive step calls the bigram
function with a list that contains only one word (indeed, the tail of the list containing the last bigram),
Haskell will match this call with the stop condition. The result of this call will simply an empty list.
Lets first proof that this indeed works:
Prelude> bigram ["Colorless", "green", "ideas", "sleep", "furiously"]
It did! To make the working of the use of pattern matching more insightful we can again write out
an artificial example in steps:
Prelude> bigram [1,2,3,4]
bigram [1,2,3,4] = [1,2] : bigram (tail [1,2,3,4])
bigram [2,3,4] = [2,3] : bigram (tail [2,3,4])
bigram [3,4] = [3,4] : bigram (tail [3,4])
bigram [4] = []
bigram [3,4] = [3,4] : []
bigram [2,3,4] = [2,3] : [3,4] : []
bigram [1,2,3,4] = [1,2] : [2,3] : [3,4] : []
Check? We are almost there now. There two things left that we should look at before we mark our
function as production ready. The first is a tiny aesthetically unpleasing detail. In the pattern of our
step condition we use the variable x, whereas we do not use this variable in the body of the function.
It is therefore not necessary to bind the list element to this variable. Fortunately, Haskell provides a
pattern that matches anything, without doing binding. This pattern is represented by an underscore.
Using this underscore, we can patch up the aesthetics of our function:
bigram :: [a] -> [[a]]
bigram [_] = []
bigram xs = take 2 xs : bigram (tail xs)
Secondly, our function fails if we apply it to an empty list:
Prelude> bigram []
[[]*** Exception: Prelude.tail: empty list
But hey! That error message looks familiar, doesn't it? Our function fails, again because we attempted
to extract a bigram from the tail of an empty list. Indeed, an empty list does not match with the pattern
of our stop condition, and therefore the recursive step is applied to it. We can solve this by adding
a pattern for an empty list:
bigram :: [a] -> [[a]]
bigram [] = []
bigram [_] = []
bigram xs = take 2 xs : bigram (tail xs)
This new pattern basically states that the list of a bigrams of an empty word list is in turn an empty
list. This assures that our function will not fail when applied to an empty list:
Prelude> bigram []
If you want to get really fancy, you could also use pattern matching to extract a bigram, rather than
using take:
bigram' :: [a] -> [[a]]
bigram' (x:y:xs) = [x,y] : bigram xs
bigram' _ = []
Now, we only need to account for two patterns: the first pattern matches when the list has at least two
elements. The second pattern matches the empty list and the list containing just one element.
Good, we are all set! We have our bigram function now... time for some applications of a bigram
language model!
1. A skip-bigram is any pair of words in sentence order. Write a function skipBigrams that extracts
skip-bigrams from a sentence as a list of binary tuples, using explicit recursion. Running your
function on ["Colorless", "green", "ideas", "sleep", "furiously"] should give the following output:
Prelude> skipBigrams ["Colorless", "green", "ideas", "sleep", "furiously"]
A few words on Pattern Matching
A straightforward application of bigrams is the identification of so-called collocations. Recall that
bigram language models exploit the observations that words do not simply combine in any random
order, that is, word order is constraint by grammatical structure. However, some combinations of
words are subject to an additional law of constraint. This law enforces a combination of two words to
occur relatively more often together than in absence of each other. Such combinations are commonly
known as collocations. Depending on the corpus, examples of collocations are:
United States
vice president
chief executive
Corpus linguists study such collocations to answer interesting questions about the combinatory
properties of words. An example of such a question concerns the combination of verbs and
prepositions: does the verb to govern occur more often in combination with the preposition by than
with the preposition with?.
In the present section, we will investigate collocations in the Brown corpus. But before we do so, we
first turn to the question of how to identify collocations. A simple but effective approach to collocation
identification is to compare the observed chance of observing a combination of two words to the
expected chance. How does this work? Well, say we have a 1000 word corpus in which the word
vice occurs 50 times, and the word president 100 times. In other words, the chance that a randomly
picked word is the word vice is p(vice) = 50/1000 = 0.05. In similar fashion, the chance that randomly
picked word is the word president is p(president) = 100/1000 = 0.1. Now what would be the chance
of observing the combination vice president if the word vice and president were "unrelated"? Well,
this would simply be the chance of observing the word vice multiplied by the chance of observing the
word president. Thus, p(vice president) = 0.05 x 0.01 = 0.005. From our thousand word corpus, we
can extract 1000 - 1 = 999 bigrams. Assume that the bigram vice president occurs 40 times, meaning
that the chance of observing this combination in our corpus is p(vice president) = 40 / 999 = 0.04. This
reveals the observed chance of observing the combination vice president is larger than the expected
chance. In fact we can quantify this difference in observed and expected chance for any two words
W1 and W2:
Equation 3.1. Difference between observed and expected chance
The observed chance of observing the combination vice president is eight times larger than the
expected chance of observing this combination. The difference between the observed and expected
chance will be large for words that occur together a lot of times, whereas it will be small for words
that also occur relatively often independent of each other.
Provided this measure of difference between the observed and expected chance, we can identify the
strongest collocations in a corpus by means of three steps:
1. Extract all the bigrams from the corpus
2. Compute the difference between the observed and expected chance for each bigram
3. Rank the bigrams based on these differences
The bigrams with the highest difference between observed and expected chance reflect the strongest
collocations. However, the difference between observed and expected chances might easily become
very large. To condense these difference values, we can represent them in logarithmic space. By doing
so, we have stumbled upon a very frequent used measure of association: the so-called Pointwise Mutual
Information (PMI). The PMI value for the combination of the vice president is:
Equation 3.2. Pointwise mutual information
Provided this association measure, we can replace step two in three steps above with: compute the
PMI between the obseved and expected chance for each bigram.
Now that we know how to identify collocations, we can apply our knowledge to the Brown corpus.
First we have to read in the contents of this corpus like we learned in the previous chapter:
*Main> h <- IO.openFile "brown.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
Good! From here on, let us first obtain a list of bigrams for this corpus:
*Main> let bgs = bigrams (words c)
*Main> head bgs
As a sanity check, we could verify whether we indeed obtained all the bigrams in the corpus. For a
corpus of n words, we expect n-1 bigrams:
*Main> length (words c)
*Main> length bgs
That looks great! Next we need to determine the relative frequency of each of these bigrams in the
corpus. That is, for each bigram we need to determine the observed chance of observing it. We could
start by determining the frequency of each bigram. We can reuse the freqList function defined in the
previous chapter to so:
*Main> Data.Map.lookup ["United","States"] (freqList bgs)
Just 392
Todo: finish this section
From bigrams to n-grams
While extracting collocations from the Brown corpus, we have seen how useful bigrams actually are.
But at this point you may be clamoring for the extraction of collocations of three or more words. For
this and many other tasks, it is useful to extract so-called n-grams for an arbitrary n. We can easily
modify our definition of bigrams to extract n-grams a specified length. Rather than always takeing
two elements, we make the number of items to take an argument to the function:
ngrams :: Int -> [a] -> [[a]]
ngrams 0 _ = []
ngrams _ [] = []
ngrams n xs
| length ngram == n = ngram : ngrams n (tail xs)
| otherwise = []
ngram = take n xs
We also cannot use pattern matching to exclude the tail when it is shorter than n. Instead, we add
a guard that ends the recursion if we cannot get the proper number of elements from the list. This
function works as you would expect:
Prelude> ngrams 3 [1..10]
Prelude> ngrams 8 [1..10]
Since this is barely worth a section, we will take this opportunity to show two other implementations
of the ngrams function. The first will be more declarative than the definition above, the second will
make use of a monad that we have not used yet: the list monad.
A declarative definition of ngrams
Some patterns emerge in the recursive definition of ngrams that correspond to functions in the
Data.List module:
1. Every recursive call uses the tail of the list. In other words, we enumerate every tail of the list,
including the complete list. The Data.List.tails function provides exactly this functionality.
2. We extract the first n elements from every tail. This is a mapping over the data that could be
performed with the map function.
3. The guards in the recursive case amount to filtering lists that do not have length n. Such filtering
can also be performed by the filter function.
Let's go through each of these patterns to compose a declarative definition of ngrams. First, we extract
the tails from the list, using the tails function:
Prelude> import Data.List
Prelude Data.List> let sent = ["Colorless", "green", "ideas", "sleep", "furiously"]
Prelude Data.List> tails sent
This gives us a list of tails, including the complete sentence. Now, we map take over each tail to
extract an n-gram. Since take requires two arguments, we use currying to bind the first argument.
For now. we will use take 2 to extract bigrams:
Prelude Data.List> map (take 2) $ tails sent
This comes close to a list of bigrams, except that we have an empty list and a list with just one member
dangling at the end. These anomalies are perfect candidates to be filtered out, so we use the filter
function in conjunction with the length function to exclude any element that is not of the given
length. To accomplish this, we apply some currying awesomeness. Remember that we can convert
infix operators to prefix operators by adding parentheses:
Prelude Data.List> (==) 2 2
Prelude Data.List> (==) 2 3
This shows that == is just an ordinary function, that just happens to use the infix notation for
convenience. Since this is an ordinary function, we can also apply currying:
Prelude Data.List> let isTwo = (==) 2
Prelude Data.List> isTwo 2
Prelude Data.List> isTwo 3
Ok, so we want to check whether a list has two elements, so we could just apply isTwo to the result
of the length function:
Prelude Data.List> isTwo (length ["Colorless","green"])
Prelude Data.List> isTwo (length [])
Or, written as a function definition:
hasLengthTwo l = isTwo (length l)
Since this function follows the canonical form f (g x), we can use function composition:
Prelude Data.List> let hasLengthTwo = isTwo . length
Prelude Data.List> hasLengthTwo ["Colorless","green"]
Our filtering expression, (==) 2 . length, turns out to be quite compact. Time to test this with our not-
yet-correct list of bigrams:
Prelude Data.List> filter ((==) 2 . length) $ map (take 2) $ tails sent
And this corresponds to the output we expected. So, we can now wrap this expression in a function,
replacing 2 by n:
ngrams' :: Int -> [b] -> [[b]]
ngrams' n = filter ((==) n . length) . map (take n) . tails
This function is equivalent to ngrams for all given lists.
You may wonder why this exercise is worthwhile. The reason is that the declarativeness of ngrams'
makes the function much easier to read. We can almost immediately see what this function does by
reading its body right-to-left, while the recursive definition requires a closer look. You will notice
that, as you get more familiar with Haskell, it will become easier to spot such patterns in functions.
A monadic definition of ngrams
As discussed in the previous chapter, each type that belongs to the Monad typeclass provides the (>>=)
function to combine expressions resulting in that type. The list type also belongs to the monad type
class. In GHCi, you can use the :info command to list the type classes to which a type belongs:
Prelude> :i []
data [] a = [] | a : [a] -- Defined in GHC.Types
instance Eq a => Eq [a] -- Defined in GHC.Classes
instance Monad [] -- Defined in GHC.Base
instance Functor [] -- Defined in GHC.Base
instance Ord a => Ord [a] -- Defined in GHC.Classes
instance Read a => Read [a] -- Defined in GHC.Read
instance Show a => Show [a] -- Defined in GHC.Show
The third line of the output shows that lists belong to the Monad type class. But how does the (>>=)
function combine expressions resulting in a list? A quick peek at its definition for the list type reveals
instance Monad [] where
m >>= k = foldr ((++) . k) [] m
So, the join operation takes a list m, applies a function k to each element and concatenates the results.
Of course, this concatenation implies that k itself should evaluate to a list, making the type signature
of k as follows: k :: a -> [a]
We will illustrate this with an example. Suppose that we would want to calculate the immediate
predecessor and successor of every number in the list [0..9]. In this case, we could use the function
\x -> [x-1,x+1] in the list monad:
Prelude> :{
l <- [0..9]
ps <- (\x -> [x-1,x+2]) l
return ps
First, the list is bound to l, then our predecessor/successor function is applied to l. Since we are using
this function in the context of the list monad, the function is be applied to every member of l. The
results of these applications is concatenated.
Experimenting with list monads may give you results that may be surprising at first sight.
For instance:
Prelude> :{
l <- [0..9]
m <- [42,11]
return m
Since [42,11] in m <- [42,11] does not use an argument, its corresponding function is \_ ->
[42,11]. Since foldr still traverses the list bound to l, the monadic computation is equal to:
Prelude> foldr ((++) . (\_ -> [42,11])) [] [0..9]
We can also extract bigrams using the list monad. Given a list of tails, we could extract the first two
words of each tail using take:
Prelude> import Data.List
Prelude Data.List> let sent = ["Colorless", "green", "ideas", "sleep", "furiously"]
Prelude Data.List> :{
t <- tails sent
l <- take 2 t
return l
That's close. However, since the list monad concatenates the results of every take 2 t expression, we
cannot directly identify the n-grams anymore. This is easily remedied by wrapping the result of take
in a list:
Prelude Data.List> :{
t <- tails sent
l <- [take 2 t]
return l
Now we get the n-grams nicely as a list. However, as in previous definitions of ngrams we have to
exclude lists that do not have the requested number of elements. We could, as we did previously, filter
out these members using filter:
Prelude Data.List> :{
filter ((==) 2 . length) $ do
t <- tails sent
l <- [take 2 t]
return l
But that would not be a very monadic way to perform this task. It would be nice if we could just choose
elements to our liking. Such a (monadic) choice function exists, namely Control.Monad.guard:
Prelude Data.List> import Control.Monad
Prelude Data.List Control.Monad> :type guard
guard :: MonadPlus m => Bool -> m ()
guard is a function that takes a boolean, and returns something that is a MonadPlus. Whoa! For now,
accept that the list type belongs to the MonadPlus type class (after importing Control.Monad). Instead
of going into the working of MonadPlus now, we will perform a behavioral study of guard:
Prelude Data.List Control.Monad> :{
l <- [0..9]
guard (even l)
return l
Funky huh? We used guard to enumerate just those numbers from [0..9] that are even. Of course, we
could as well use guard in our bigram extraction to filter lists that are not of a certain length:
Prelude Data.List Control.Monad> :{
t <- tails sent
l <- [take 2 t]
guard (length l == 2)
return l
Ain't that beautiful? We applied a guard to pick just those elements that are of length 2, or as you might
as well say, we put a constraint on the list requiring elements to be of length 2. We can easily transform
this expression to a function, by making the n-gram length and the list arguments of that function:
ngrams'' :: Int -> [a] -> [[a]]
ngrams'' n l = do
t <- tails l
l <- [take n t]
guard (length l == n)
return l
As you can conclude from the previous sections, there is often more than one way to implement a
function. In practice you will want to pick a declaration that is readable and performant. In this case,
we think that the declarative definition of ngrams is the most preferable.
1. Rewrite the skipBigram function discussed in the section called “Exercises” without explicit
recursion, either by defining it more declaratively or using the list monad. Hint: make use of the function.
Lazy and strict evaluation
You may have noticed that something curious goes on in Haskell. For instance, consider the following
GHCi session:
Prelude> take 10 $ [0..]
The expression [0.. is the list of numbers from zero to infinity. Obviously, it is impossible to store
an infinite list in finite memory. Haskell does not apply some simple trick, since it also works in less
trivial cases. For instance:
Prelude> take 10 $ filter even [0..]
This also works for your own predicates:
Prelude> let infinite n = n : infinite (n + 1)
Prelude> take 3 $ infinite 0
In most other programming languages, this computation will never terminate, since it will go into
an infinite recursion. Haskell, however, won't. The reason is that Haskell uses lazy evaluation - an
expression is only evaluated when necessary. For instance, taking three elements from infinite
results in the following evaluations:
infinite 0
0 : infinite 1
0 : (1 : infinite 2)
0 : (1 : (2 : infinite 3))
0 : (1 : (2 : (3 : infinite 4)))
Once take has consumed enough elements from infinite, the tail of the list is the expression
infinite 4. Since take does not need more elements, the tail is never evaluated. Lazy evaluation
allows you to do clever tricks, such as defining infinite lists. The downside is that it is often hard to
predict when an expression is evaluated, and what effect that has on performance of a program.
Todo: lazy evaluation and folds.
Suffix arrays
In this chapter, we have seen how you could extract an n-gram of a given n from a list of words,
characters, groceries, or whatever you desire. You can also store n-gram frequencies in a Map, to build
applications that quickly need the frequency (or probability) of an n-gram in a corpus. What if you
would encounter an application where you need access to n-grams of any length? Any! From unigrams
to 'almost the length of your corpus'-grams. Obviously, if your corpus contains m elements, storing
frequencies of all 1..m-grams would make your program a memory hog.
Fortunately, it turns out that there is a simple and smart trick to do this, using a data structure called
suffix arrays. First, we start with the corpus, and a parallel list or array where each element contains an
index that can be seen as a pointer into the corpus. The left side of figure Figure 3.1, “Constructing a
suffix array” shows the initial state for the phrase "to be or not to be". We then sort the array of indices
by comparing the elements they point to. For instance, we could compare the element with index 2
("or") and the element with index 3 ("not"). Since "not" is lexicographically ordered before "or", the
list of indices should be sorted such that the element holding index 3 comes before 2. When two indices
point to equal elements, e.g. 0 and 4 ("to"), we move on to the element that succeed both instances of
"to", respectively "be" and "be". And we continue such comparisons recursively, until we find out that
one n-gram is lexicographically sorted before the other (in this case, 4 should come before 0, since "to
be" is lexicographically sorted before "to be or". The right side of figure Figure 3.1, “Constructing a
suffix array” shows how the indices will be sorted after applying this sorting methodology.
Figure 3.1. Constructing a suffix array
to 0
be 1
or 2
not 3
to 4
be 5
Unsorted indices
to 5
be 1
or 3
not 2
to 4
be 0
Sorted indices
After sorting the list of indices in this manner, the index list represents an ordered list of n-grams
within the corpus. The length of the n-gram does not matter, since elements and their suffixes were
compared until one element could be sorted lexicographically before the other. This ordering also
implies that we can use a binary search to check whether an n-gram occurred in the corpus, and if so,
how often. But more on that later...
Of course, as a working programmer you can't wait to fire up your text editor to implement suffix
arrays. It turns out to be simpler than you might expect. But, we need to introduce another data type
first, the vector. It is a data type that is comparable to arrays in other programming languages. Vectors
allow for random access to array elements. So, if you want to access the n-th element of a vector, it
can be accessed directly, rather than first traversing the n-1 preceding elements as in a list. Vectors are
provided in Haskell as a part of the vector package that can be installed using cabal. We can construct
a Vector from a list and convert a Vector to a list:
Prelude> Data.Vector.fromList ["to","or","not","to","be"]
fromList ["to","or","not","to","be"] :: Data.Vector.Vector
Prelude> Data.Vector.toList $ Data.Vector.fromList ["to","or","not","to","be"]
The (!) function is used to access an element:
Prelude> (Data.Vector.fromList ["to","or","not","to","be"]) Data.Vector.! 3
There's also a safe access function, (!?), that wraps the element in a Maybe. Nothing is returned
when you use an index that is 'outside' the vector:
Prelude> (Data.Vector.fromList ["to","or","not","to","be"]) Data.Vector.! 20
"*** Exception: ./Data/Vector/Generic.hs:222 ((!)): index out of bounds (20,5)
Prelude> (Data.Vector.fromList ["to","or","not","to","be"]) Data.Vector.!? 20
Prelude> (Data.Vector.fromList ["to","or","not","to","be"]) Data.Vector.!? 3
Just "to"
That enough for now. The primary reason why Vector is a useful type here, is because we want random
access to the corpus during the construction of the suffix array. After construction, it is also useful
for most tasks to be able to access the indices randomly. Alright, first we create a data type for the
suffix array:
import qualified Data.Vector as V
data SuffixArray a = SuffixArray (V.Vector a) (V.Vector Int)
deriving Show
It says exactly what we saw in the figure above: a suffix array consists of a data vector (in our case
a corpus) and a vector of indices, respectively V.Vector a and V.Vector Int. Ideally, we would like
to construct a suffix array from a list. However, to do this, we need a sorting function. The Data.List
module contains the sortBy function that sorts a list according to some ordering function:
*Main> :type Data.List.sortBy
Data.List.sortBy :: (a -> a -> Ordering) -> [a] -> [a]
So, it takes a comparison function that should compare two elements, and that returns Ordering.
Ordering is a data type that specifies... order. There are three constructors: LT, EQ, andGT, these
constructors indicate respectively that the first argument is less than, equal to, or greater than the
second argument.
We will use sortBy to sort the list of indices. Since the ordering of the indices is determined by
elements of the data array, to which the indices refer, the comparison function that we provide for
sorting the index array requires access to the data array. So, our function will compare (sub)vectors,
indicated by their indices. This will work, since the Data.Vector data type is of the Ord type class,
meaning that the operators necessary for comparisons are provided. Our comparison function can be
written like this:
saCompare :: Ord a => (V.Vector a -> V.Vector a -> Ordering) ->
V.Vector a -> Int -> Int -> Ordering
saCompare cmp d a b = cmp (V.drop a d) (V.drop b d)
To allow a user of our function to impose their own sorting order (maybe the want to make a reversibly
offered suffix array), we saCompare requires a comparison function as its first argument. The second
argument is the data vector, and the final two arguments are the indices to be compared. We can get
the subvectors represented by the two indices by using the Data.Vector.drop function. Suppose,
if we want the element at index two, we can just drop the first two arguments, since we start counting
at zero. We then use the provided comparison function to compare the two subvectors.
Now we can create the function that actually creates a suffix array:
import qualified Data.List as L
suffixArrayBy :: Ord a => (V.Vector a -> V.Vector a -> Ordering) ->
V.Vector a -> SuffixArray a
suffixArrayBy cmp d = SuffixArray d (V.fromList srtIndex)
where uppBound = V.length d - 1
usrtIndex = [0..uppBound]
srtIndex = L.sortBy (saCompare cmp d) usrtIndex
This function is fairly simple, first we create the unsorted list of indices and bind it to usrtIndex.
We construct this list by using a range. A range contains the indicated lower bound and upper bound,
and all integers in between;
*Main> [0..9]
We retrieve the upper bound using the Data.Vector.length function by subtracting one,
since we are counting from zero. We then obtain the sorted index (srtIndex) by using the
Data.List.sortBy function. This function takes a comparison function as its first argument and a
list as its second argument:
*Main> :type Data.List.sortBy
Data.List.sortBy :: (a -> a -> Ordering) -> [a] -> [a]
We can just plug in our saCompare function, which we pass a comparison function, and the data
vector. Finally, we use the SuffixArray constructor to construct a SuffixArray, converting the list of
indices to a vector. For convenience, we can also add a function that uses Haskell's compare function
that uses the default sorting order that is imposed by the Ord typeclass:
suffixArray :: Ord a => V.Vector a -> SuffixArray a
suffixArray = suffixArrayBy compare
Neat! But as you have noticed by now, every serious data type has fromList and toList functions,
so ours should have those as well. fromList is really simple; we can already construct a suffix array
from a Vector using the suffixArray function. So, we just need to convert a list to a Vector, and
pass it to suffixArray:
fromList :: Ord a => [a] -> SuffixArray a
fromList = suffixArray . V.fromList
Easy huh? The toList is a bit more involved. First we have to decide what it should actually return.
Providing the data vector as a list is not very useful, it's probably what someone started with. Returning
a list of indices is more useful, but then we shift the burden off retrieving the n-grams that every index
represents to the user of our suffix array. The most useful thing would be to return a list of all n-grams
(of any length). So, for the phrase "to be or not to be", we want to return the following elements:
• ["be"]
• ["be","or","not","to","be"]
• ["not","to","be"]
• ["or","not","to","be"]
• ["to","be"]
• ["to","be","or","not","to","be"]
To achieve this, we need to extract the subvector for each index, in the order that the sorted vector
of indices indicates. We can then convert each subvector to a list. We can use Data.Vector.foldr
function to traverse the vector, constructing a list for each index. We will accumulate these lists in
(yet another) list. Please welcome toList:
toList :: SuffixArray a -> [[a]]
toList (SuffixArray d i) = V.foldr vecAt [] i
where vecAt idx l = V.toList (V.drop idx d) : l
The vecAt function extracts a subvector starting at index idx, converts it to a list. We form a new
list, with the accumulator as the tail, and the newly constructed 'subvector list' as the head. We use
foldr to ensure that the list that is being constructed is in the correct order - since the accumulator
becomes the tail, a foldl would make the first subarray the last in the list. Time to play with our
new data type a bit:
*Main> toList $ fromList ["to","be","or","not","to","be"]
Excellent, just as we want it: we get an ordered list of all n-grams in the corpus, for the maximum
possible n. We can use this function to extract all bigrams:
*Main> filter ((== 2) . length) $ map (take 2) $ toList $ \
fromList ["to","be","or","not","to","be"]
We extract the first two elements of each n-gram. This also gives us one unigram (the last token of
the corpus), so we also have to filter the list for lists that contain two elements.
After some celebrations and a cup of tea, it is time to use suffix arrays to find the frequency of a word.
To do this, we use a binary search. For quick accessibility, we create a function comparable to the
toList method, but returning a Vector of Vector, rather than a list of list:
elems :: SuffixArray a -> V.Vector (V.Vector a)
elems (SuffixArray d i) = vecAt i
where vecAt idx = V.drop idx d
Note that we can use in this case, since it maps a function over all elements of
vector, returning a vector:
*Main> :type :: (a -> b) -> V.Vector a -> V.Vector b
Note: if you have a computer science background, you might want to skip the next paragraphs.
To be able to count the number of occurrences of an n-gram in the suffix array, we need to locate the
n-gram in the suffix array first. We could just traverse the array from beginning to the end, comparing
each element to the n-gram that we are looking for. However, this is not very inefficient. During every
search step, we exclude just one element. For instance, if we have the numbers 0 to 9 and have to
find the location of the number 7, the first search step would just exclude the number 0, leaving eight
potential candidates (Figure 3.2, “Linear search step”).
Figure 3.2. Linear search step
However, if we know that the vector of numbers is sorted, we can devise a more intelligent strategy.
As a child, you probably played number guessing games. In one variant of the game, you would guess
a number, and the person knowing the correct number would shout "smaller", "larger" or "correct".
Being a smart kid, you would probably not start guessing at 1 if you had to guess a number between 1
and 100. Usually, you'd start somewhere halfway the range (say 50), and continue halfway the 1..50
or 51..100 range if the number was smaller or greater than 50.
The same trick can be applied when searching a sorted vector. If you compare a value to the element
in the middle, you remove cut half of the search space (if initial guess was not correct). This procedure
is called a binary search. For instance, Figure 3.3, “Binary search step” shows the first search step
when applying a binary search to the example in Figure 3.2, “Linear search step”.
Figure 3.3. Binary search step
The performance of binary search compared to linear search should not be underestimated: the time
of a linear search grows linearly with the number of elements (yes, we like pointing out the obvious),
while time of a binary search grows logarithmically. Suppose that we have a sorted vector of 1048576
elements, a linear search would at most take 1048576 steps, while a binary search takes at most 20
steps. Pretty impressive right?
On to our binary search function. binarySearchByBounded finds the index of an element in a
Vector, wrapped in Maybe. If the element has multiple occurrences in the Vector, just one index is
returned. If the element is not in the Vector, Nothing is returned.
binarySearchByBounded :: (Ord a) => (a -> a -> Ordering) -> V.Vector a ->
a -> Int -> Int -> Maybe Int
binarySearchByBounded cmp v e lower upper
| V.null v = Nothing
| upper < lower = Nothing
| otherwise = case cmp e (v V.! middle) of
LT -> binarySearchByBounded cmp v e lower (middle - 1)
EQ -> Just middle
GT -> binarySearchByBounded cmp v e (middle + 1) upper
where middle = (lower + upper) `div` 2
binarySearchByBounded takes a host of arguments: a comparison function, the (sorted) vector (v,
the element to search for (e), and lower (lower) and upper bound (upper) indices of the search space.
The function works just like we described above. First we have to find the middle of the current search
space, we do this by averaging the upper and lower bounds and binding it to middle. We then compare
the element at index middle in the vector to e . If both are equal (EQ), then we are done searching,
and return Just middle as the index. If e is smaller than (LT) the current element, we search in
the lower half of the search space (lower..middle-1). If e is greater than (GT) the current element,
we search in the upper half of the search space (middle+1..upper). If e does not occur in the search
space, upper will become smaller than lower when we have exhausted the search space.
Let's define two convenience functions to make binary searches a bit simpler:
binarySearchBounded :: (Ord a) => V.Vector a -> a -> Int -> Int -> Maybe Int
binarySearchBounded = binarySearchByBounded compare
binarySearchBy :: (Ord a) => (a -> a -> Ordering) -> V.Vector a -> a ->
Maybe Int
binarySearchBy cmp v n = binarySearchByBounded cmp v n 0 (V.length v - 1)
binarySearch :: (Ord a) => V.Vector a -> a -> Maybe Int
binarySearch v e = binarySearchBounded v e 0 (V.length v - 1)
binarySearchBounded calls binarySearchByBounded, using Haskell's standard compare
function. binarySearchBy calls binarySearchByBounded, binding the upper and lower bounds
to the lowest index of the array (0) and the highest (the size of the Vector minus one). Finally,
binarySearch combines the functionality of binarySearchBounded and binarySearchBy, Let's
give the binary search functionality a try:
*Main> binarySearch (V.fromList [1,2,3,5,7,9]) 9
Just 1
*Main> binarySearch (V.fromList [1,2,3,5,7,9]) 10
Just 5
*Main> binarySearch (V.fromList [1,2,3,5,7,9]) 10
Great! Let's make a step in between, returning to suffix arrays. Say that you would want to write a
contains function that returns True if an n-gram is in the suffix array, or False otherwise. Easy right?
Your first attempt may be something like:
*Main> let corpus = ["to","be","or","not","to","be"]
*Main> binarySearch (elems $ fromList corpus) $ Data.Vector.fromList ["or","not", "to", "be"]
Just 3
Nice, right? But try this example:
*Main> binarySearch (elems $ fromList corpus) $ Data.Vector.fromList ["or","not"]
You can almost hear the commentator of Roger Wilco and the Time Rippers in the background, right?
Right! Of course, the element that we are looking for contains the n-gram of the maximum length ("or
not to be"). That is why the first example worked, while the second did not. So, we have to apply the
binary search to something that only contains bigrams in this case:
*Main> :{
*Main| binarySearch
*Main| ( (Data.Vector.take 2) $ elems $ fromList corpus) $
*Main| Data.Vector.fromList ["or","not"]
*Main| :}
Just 3
That did the trick. Writing the contains function is now simple:
contains :: Ord a => SuffixArray a -> V.Vector a -> Bool
contains s e = case binarySearch (restrict eLen s) e of
Just _ -> True
Nothing -> False
where eLen = V.length e
restrict len = (V.take len) . elems
To find the frequency of an element in a Vector, we have to do a bit more than locating one instance
of that element. One first intuition could be to find the element, and scan upwards and downwards to
find how many instances of the element there are in the Vector. However, there could be millions of
such elements. Doing a linear search is, again, not very efficient. So, we should apply a binary search,
but not just to find one instance of the element, but specifically the first and the last.
Such search functions are very comparable to the binarySearchByBounds function that we wrote
earlier. Let's start with finding the first index in the Vector where a specified element occurs. Suppose
that we do a binary search again: if the element in the middle of our search space is greater than the
element, we want to continue searching in the lower half of the search space. If the element in the
middle is smaller than the element, we want to continue searching in the upper half of the search
space. If the middle is however equal to the element, we do not stop searching, but continue searching
the lower half. We still keep the element that was equal in the search space, since it may have been
the only instance of that element. This gives us the following lowerBoundByBounds function and
corresponding helpers:
lowerBoundByBounds :: Ord a => (a -> a -> Ordering) -> V.Vector a -> a ->
Int -> Int -> Maybe Int
lowerBoundByBounds cmp v e lower upper
| V.null v = Nothing
| upper == lower = case cmp e (v V.! lower) of
EQ -> Just lower
_ -> Nothing
| otherwise = case cmp e (v V.! middle) of
GT -> lowerBoundByBounds cmp v e (middle + 1) upper
_ -> lowerBoundByBounds cmp v e lower middle
where middle = (lower + upper) `div` 2
lowerBoundBounds :: Ord a => V.Vector a -> a -> Int -> Int -> Maybe Int
lowerBoundBounds = lowerBoundByBounds compare
lowerBoundBy :: Ord a => (a -> a -> Ordering) -> V.Vector a -> a -> Maybe Int
lowerBoundBy cmp v e = lowerBoundByBounds cmp v e 0 (V.length v - 1)
lowerBound :: Ord a => V.Vector a -> a -> Maybe Int
lowerBound = lowerBoundBy compare
Searching the last index in the Vector where the element occurs, follows a comparable procedure. We
search as normal, however if the element is equal to the middle we search the upper half of the search
space including the element that we found to be equal. Give the floor to upperBoundByBounds and
upperBoundByBounds :: Ord a => (a -> a -> Ordering) -> V.Vector a -> a ->
Int -> Int -> Maybe Int
upperBoundByBounds cmp v e lower upper
| V.null v = Nothing
| upper <= lower = case cmp e (v V.! lower) of
EQ -> Just lower
_ -> Nothing
| otherwise = case cmp e (v V.! middle) of
LT -> upperBoundByBounds cmp v e lower (middle - 1)
_ -> upperBoundByBounds cmp v e middle upper
where middle = ((lower + upper) `div` 2) + 1
upperBoundBounds :: Ord a => V.Vector a -> a -> Int -> Int -> Maybe Int
upperBoundBounds = upperBoundByBounds compare
upperBoundBy :: Ord a => (a -> a -> Ordering) -> V.Vector a -> a -> Maybe Int
upperBoundBy cmp v e = upperBoundByBounds cmp v e 0 (V.length v - 1)
upperBound :: Ord a => V.Vector a -> a -> Maybe Int
upperBound = upperBoundBy compare
Note that we add one to the middle in this case. This is to avoid landing in an infinite recursion when
middle is lower plus one, and the element is larger than or equal to the element at middle. Under
those circumstances, lower and upper would be unchanged in the next recursion.
Great. I guess you will now be able to write that function in terms of lowerBoundByBounds and
frequencyByBounds :: Ord a => (a -> a -> Ordering) -> V.Vector a -> a ->
Int -> Int -> Maybe Int
frequencyByBounds cmp v e lower upper = do
lower <- lowerBoundByBounds cmp v e lower upper
upper <- upperBoundByBounds cmp v e lower upper
return $ upper - lower + 1
frequencyBy :: Ord a => (a -> a -> Ordering) -> V.Vector a -> a ->
Maybe Int
frequencyBy cmp v e = frequencyByBounds cmp v e 0 (V.length v - 1)
frequencyBounds :: Ord a => V.Vector a -> a -> Int -> Int -> Maybe Int
frequencyBounds = frequencyByBounds compare
frequency :: Ord a => V.Vector a -> a -> Maybe Int
frequency = frequencyBy compare
This function works as expected:
*Main> frequency (V.fromList [1,3,3,4,7,7,7,10]) 7
Just 3
*Main> frequency (V.fromList [1,3,3,4,7,7,7,10]) 5
We can use this with our suffix array now:
*Main> let corpus = ["to","be","or","not","to","be"]
*Main> let sa = fromList corpus
*Main> containsWithFreq sa $ Data.Vector.fromList ["not"]
Just 2
*Main> containsWithFreq sa $ Data.Vector.fromList ["not"]
Just 1
*Main> containsWithFreq sa $ Data.Vector.fromList ["jazz","is","not","dead"]
*Main> containsWithFreq sa $ Data.Vector.fromList ["it","just","smells","funny"]
1. Write a function mostFrequentNgram with the following type signature:
mostFrequentNgram :: Ord a => SuffixArray a -> Int -> Maybe (V.Vector a, Int)
This function extracts the most frequent n-gram from a suffix array, where the suffix array and n
are given as arguments. The function should continue a pair of the n-gram and the frequency as
a typle wrapped in Maybe. If no n-gram could be extracted (for instance, because the suffix array
contains to few elements), return Nothing.
2. Use mostFrequentNgram to find the most frequent bigram and trigram in the Brown corpus.
3. frequencyByBounds is not as efficient as it could be: it performs a search of the full Vector twice.
A more efficient solution would be to narrow down the search space until the first match is found,
and then using lowerBoundByBounds and upperBoundByBounds to search the lower and upper
half of the search space. Modify frequencyByBounds to use this methodology.
Markov models
At the beginning of this chapter we mentioned that n-grams can be exploited to model language. While
they may not be so apt as computational grammars, n-grams do encode some syntax albeit local. For
instance, consider the following to phrases:
the plan was
* plan the was
The first phrase is clearly grammatical, while the second is not. We could neatly encode this using
a syntax rule, but we could also count how often both combinations of words occur in a large text
corpus. The first phrase is likely to occur a few times, while the second phrase is not likely to occur.
Or more formally, the probability that we encounter this plan was occurs in a random text is higher
than the probability that plan this was occurs:
Of course, we could also try to find the most grammatical of two sentences by comparing the
probabilities of the sentences. So, if we have a sentence consisting of the words w0..n and a sentence
consisting of the words v0..m that both aim to express the same meaning and the following is true:
We could conclude that the use of w0..n is preferred over v0..m, since w0..n is either more grammatical
or more fluent. So, how do we estimate the probability of such a sentence? Good question, at first
sight it seems pretty easy. We simply count how often a sentence occurs in a text corpus, and divide
it by the total number of sentences in a corpus:
Equation 3.3. Estimating the probability of a sentence
Here C is a counter function, and N is the total number of sentences in a corpus. While this is a
theoretically sound method for estimating the probability, it does not work in practice. As ingenious
as human language is, we can construct an infinite number of grammatical sentences. So, to be able to
estimate the probability we would need an infinite text corpus, since not every grammatical sentence
will occur in a finite corpus. Given that we only have a finite text corpus, we would simply give a
probability of zero to many perfectly grammatical sentences. We encounter so-called data sparseness.
This is nasty, because it interferes with our goal to compare the quality of sentences.
Fortunately for us, some smart people have thought about this problem, and came up with a pretty
elegant solution (or `workaround' as we programmers like call it). To get to the solution, we have to
make an intermediate step. This intermediate step does not immediately solve our problem, but sets
the stage for the solution. We can decompose the probability of a sentence p(w0..n) into a series of
conditional probabilities:
Equation 3.4. The probability of a sentence as a Markov chain
Before this gets too confusing, let's write down how you would estimate the probability of the
sentence Colorless green ideas sleep furiously in this manner: p(Colorless) p(green|Colorless)
p(ideas|Colorless green) p(sleep|Colorless green ideas) p(furiously|Colorless green ideas sleep).
Simple huh? Now, how do we estimate such a conditional probability? Formally, this is estimated in
the following manner:
That is all nice and dandy, but as you may already see, this does not solve our problem with data
sparseness. For if we want to estimate p(furiously|Colorless green ideas sleep), we need counts of
Colorless green ideas sleep and Colorless green ideas sleep furiously. Even if we decompose the
probability of a sentence into conditional probabilities, we need counts for the complete sentence.
However, if we look at the conditional probability of a word, the following often holds:
Equation 3.5. Approximation using the Markov assumption
More formally, this is a process with the Markov property: prediction of the next state (word) is only
dependent on the current state. Of course, we can easily calculate our revised conditional probability:
Equation 3.6. The conditional probability of a word using the Markov
That spell worked! We only need counts of... unigrams (1-grams) and bigrams to estimate the
conditional probability of each word. This is a bigram language model, which we can use to estimate
to probability of a sentence:
Equation 3.7. The probability of a sentence using a bigram model
In practice it turns out that knowledge of previous states can help a bit in estimating the conditional
probability of a word. However, if we increase the context too much, we run into the same data
sparseness problems that we solved by drastically cutting the context. The consensus is that for
most applications a trigram language model provides a good trade-off between data availability and
estimator quality.
The implementation of a bigram Markov model in Haskell should now be trivial. If we have a
frequency map of unigrams and bigrams of the type (Ord a, Integral n) => Map [a] n, we could write
a function that calculates , or more generally :
import qualified Data.Map as M
import Data.Maybe (fromMaybe)
pTransition :: (Ord a, Integral n, Fractional f) =>
M.Map [a] n -> a -> a -> f
pTransition ngramFreqs state nextState = fromMaybe 0.0 $ do
stateFreq <- M.lookup [state] ngramFreqs
transFreq <- M.lookup [state, nextState] ngramFreqs
return $ (fromIntegral transFreq) / (fromIntegral stateFreq)
Now we write a function that extracts all bigrams, calculates the transition probabilities and takes the
product of the transition probabilities:
pMarkov :: (Ord a, Integral n, Fractional f) =>
M.Map [a] n -> [a] -> f
pMarkov ngramFreqs =
product . map (\[s1,s2] -> pTransition ngramFreqs s1 s2) . ngrams 2
This function is straightforward, except perhaps the product function. product calculates the
product of a list:
Prelude> :type product
product :: Num a => [a] -> a
Prelude> product [1,2,3]
Chapter 4. Distance and similarity
Chapter 5. Classification
Many natural language processing tasks require classification, you want to find out to which class a
particular instance belongs. To make this more concrete, we give three examples:
Authorship attribution: suppose that you were given a text, and have to pick the correct author
of the text from three proposed authors.
Part of speech tagging: in part of speech tagging, words are classified morphosyntactically. For
instance, we could classify the word 'loves' in the statement "John loves Mary" to be a verb.
Fluency ranking: in natural language generation, we want to find out whether a sentence produced
by a generation system is fluent or not fluent.
Such classifications can be made based on specific characteristics of the instance that we want to
specify. These characteristics are called features in natural language processing jargon. Suppose that
you were asked to determine the author of a text, and know that Jack tends to write short sentences,
while Steven and Marie tend to write long sentences. Now, if you were given a text with mainly short
sentences, who would you attribute the text to? Probably Jack, right? Average sentence length is one
possible feature to classify the text by its author.
More formally speaking, we want to estimate p(y|x) , the probability of an event (classification), given
a context. For instance, in authorship attribution, the classification being a specific author is an event,
while the text is the context. In part of speech tagging, the classification of a word as verb is an event,
the word and surrounding words are the context.
In this chapter, we will look at linear classifiers. A linear classification can make a classification based
on a linear combination of features. To give an example, consider Figure 5.1, “Linear and non-linear
classifiers”. Here we see two classes of objects, that can be separated using just two features (f1 and
f2). One class is tends to have high f1 values, the other high f2 values. The figure also shows two
classifiers, c1 and c2, that successfully separate both classes. c1 is a linear classifier, as it is a linear
combination of f1 and f2. c2, on the other hand, is not a linear classifier: the effect of f1 becomes
weaker as f2 increases.
Figure 5.1. Linear and non-linear classifiers
How do we find such functions? Doing it manually is not practical - realistic models for natural
language processing classification use thousands to millions of features. Finding such functions is an
art in itself, and is usually called machine learning. Machine learning methods learn such classifiers
through training material. Machine learning methods are a topic by themselves, so in this chapter we
will mainly look at the application of classifiers obtained through machine learning.
This may all seem somewhat abstract at this point, but things will get clearer as we dive into real
classifiers. The thing to remember now is that we want to attach a particular class label to instances,
based on features of that instance, and we will do this using linear classifiers.
Naive Bayes classification
Maximum entropy classification
An important disadvantage of naive Bayes modelling is that it has strong feature independence
assumptions. Often, it is not clear whether features are dependent, or you simply do not want to care.
For instance, coming back to the task of authorship attribution. Suppose that you made a model that
uses the average sentence length as a feature amongst others. Now you got a fantastic idea, you want
to add some features modeling syntactic complexity of sentences in a text. Such features may add
new cues to the model, but syntactic complexity also has a correlation with sentence length. In such
situations, naive Bayes models may fail, since they see these features as independent contributors to
a classification.
One class of models that do not assume independent features are maximum entropy models. We can
almost hear you think "isn't classification supposed to minimize uncertainty"? That's a very good
question, that we will come to in a moment. First, we have to ask a basic question: given a collection
of training instances, what is a good model? Think about this for a moment, without diving into theory
and technicalities.
The answer is pretty simple: since the training data is (supposed to be) a representative sample of
reality, a good model would predict the training data. What does it mean to predict the training data?
You may remember from high school math that you could calculate the expected value of a random
variable given a probability distribution. For instance, if you play a coin tossing game, you can
calculate the (average) profit or loss given enough tosses. Suppose that a friend has a biased coin, with
p(heads) = 0.7 and p(tails) = 0.3. Winning gives you Euro 1.50, when losing, you pay 1 Euro. The
expected outcome of choosing tails is 0.7 * -1 + 0.3 * 1.50 # -0.25. Not such a good bet, huh?
If we know the expected value from the observation of repeated coin flips (the training data), we can
make a model that gives the same outcome. If we know the payments, finding the model analytically
is trivial. What if we do the same for features? We can calculate the feature value in the training data:
Equation 5.1. Calculating the empirical value of a feature
It's easier than it looks: the empirical value of a feature fi is the sum of the multiplication joint
probability of a context and an event in the training data and the value of fi for that context and event.
We can also calculate the expected value of a feature fi according to the conditional model p(y|x):
Equation 5.2. Calculating the expected value of a feature
Since , and the model only estimates the conditional probability p(y|x), the probability of the context
in the training data, , is used. To make the model predict the training data, a constraint is added for
each feature fi during the training of the model, such that:
Equation 5.3. Constraining the expected value to the empirical value
For any non-trivial model, the model that satisfies these constraints cannot be found analytically. In
fact, there is normally even an infinite number of models. So, then the question becomes, which model
do we use? Consider, for example, the classifiers in Figure 5.2, “Two competing models”. While both
classifiers separate instances of both classes neatly, c2 is the better classifier; it separates the classes
with a wider margin than c1, and as such has more tolerance with respect to unseen instances that fall
outside the current class boundaries. For instance, if an instance has a high value for f1, and a slightly
higher value for f2, c1 attribute this instance to the other class while there is no reason to believe that
this is true. Or in other words, c1 has a bias.
Figure 5.2. Two competing models
c1 c2
Now the idea is clear: we want a model that satisfies a set of constraints, but also has as few assumptions
as possible.
Let's forget those constraints for a moment and get back to something simple, like coin flipping. We
have two possible outcomes, head and tail. If we have no assumptions about the coin being biased and
such, we (should) believe that the probability of getting head or tail is half-half. And what if we model
dice roles? If we believe that the (cube) dice is not biased and no trickery is involved, the probability
of each outcome should be 1/6th. In both cases, the outcomes have a uniform distribution, meaning
that every outcome is equally probable. In the uniform distribution, the probability of a particular
outcome is , where is the number of possible outcomes. In the uniform distribution, uncertainty is at
its maximum. If we know that p(tails) = 0.9, we know pretty certain that a coin flip will result in tails.
However, if p(tails) = 0.5, we are uncertain about the outcome. In fact, there is no possible distribution
that has a higher uncertainty. A measure of uncertainty is entropy.
So our model should be uniform as possible, while still obeying the constraints that are imposed. We
can find the most uniform model by maximizing entropy.
More to be done...
Chapter 6. Information retrieval
Chapter 7. Part of speech tagging
In the last episode, you have seen how n-gram language models can be used to model structure of
language, purely based on words. In this chapter, we will make a further abstraction and will try to
find proper part of speech tags (also named morphosyntactic tags) for words. Part of speech tags give
relevant information about the role of a word in its narrow context. It may also provide information
about the inflection of a word. POS tags are a valuable part of a language processing pipeline, they
provide useful information to other components such as a parser or a named-entity recognizer.
There is no such thing as a standard set of part of speech tags (let's call them 'POS tags' from now
on). Just like programming languages, text editors, and operating systems, the tag set that people use
depends on the task at hand and taste. For our purposes, we will use the Brown tag set1.
This is a sentence from the Brown corpus that is annotated with tags:
A/AT similar/JJ resolution/NN passed/VBD in/IN the/AT Senate/NN by/IN a/AT vote/NN of/IN 29-5/
CD ./.
The notation here is very simple: as our previous fragments of the Brown corpus the sentence is pre-
tokenized. However, each word is amended by a POS tag that indicate the role of the world. For
instance, the word 'a' is an article, 'similar' an adjective, and 'resolution' a singular common noun.
Corpora, such as the Brown corpus only provide POS tags for a small amount of sentences that occur
in corpus. Being a working programmer, you will deal with new data that does not occur in the Brown
corpus. Now, wouldn't it be nice to have a set of functions that could add POS tags to untagged data?
Software that performs this task is called a POS tagger or morphosyntactic tagger, and this is exactly
the thing we will build in this chapter.
In the data provided with this book, you will find the file brown-pos-train.txt. Open this file
with a text file viewer or text editor, and look at the five first sentences. Try to find out what the
tags mean using the description of the Brown tag set.
Frequency-based tagging
In one of the simplest forms of tagging, we just assign the most frequent POS tag for a token in the
training data to a token in untagged data. That's right, the most frequent tag, because a token can have
more than one tag. Consider the following two sentences:
I wouldn't trust him.
He put money in the family trust.
Both sentences contain the word 'trust'. However, 'trust' has different roles in different roles in both
sentences. In the first sentence 'trust' is a verb, in the second sentence it is a noun. So, for many tokens
we will have the choice of multiple tags. If we tag the token with the most frequent tag, we will
frequently tag tokens incorrectly, but it is a first step.
To ease handling of tokens and tags, we will make type aliases for tokens and tags and define a new
datatype for training instances, aptly named TrainingInstance:
1A full description of the Brown tag set can be found at:
Part of speech tagging
type Token = String
type Tag = String
data TrainingInstance = TrainingInstance Token Tag
deriving Show
The Token and Tag aliases will allow us to write clean function signatures. The TrainingInstance
data type has only one constructor, TrainingInstance. The data type derives from the Show typeclass,
which allows us to get a String representation of an instance2. We can use this constructor to create
training instances:
*Main> TrainingInstance "the" "AT"
TrainingInstance "the" "AT"
*Main> TrainingInstance "pony" "NN"
TrainingInstance "pony" "NN"
Since our first POS tagger is trained purely on tokens and tags, and requires no sentential information,
the corpus will be represented as a list of TrainingInstance. Since we can use the words function to
tokenize the corpus, the task at hand is to convert a list of strings of the format "token/tag" to a list
of TrainingInstance. This is done by splitting the String on the forward slash character (/). We can
use the break function to break the string on the first element for which the supplied function is true.
For instance:
Prelude> break (== '/') "the/AT"
This is a good start, we would only have to chop off the first character of the second element in
the tuple. However, there is another problem: although a tag can never contain a slash, a token can.
Consequently, we should break the string on the last slash, rather than the first. A cheap solution to
this problem could be to reverse the string, applying break, and then reversing the results again. We
will take a more sophisticated route, and write our own function:
rsplit :: Eq a => a -> [a] -> ([a], [a])
rsplit sep l = let (ps, xs, _) = rsplit_ sep l in
(ps, xs)
rsplit_ :: Eq a => a -> [a] -> ([a], [a], Bool)
rsplit_ sep = foldr (splitFun sep) ([], [], False)
where splitFun sep e (px, xs, True) = (e:px, xs, True)
splitFun sep e (px, xs, False)
| e == sep = (px, xs, True)
| otherwise = (px, e:xs, False)
The core business happens in the rsplit_ function, it splits a list in the part before the last instance
of sep (the prefix) and the part after (the suffix). It does this by folding over the input list from right to
left. The accumulator is a tuple that holds the prefix list, the suffix list, and a Bool indicating whether
the separator was encountered. The function provided to the fold acts upon this Bool:
If the Bool is True, the separator was seen, and the current element is added to the prefix list.
If the Bool is False, the separator was not seen yet. If the current element is equal to the separator,
the Bool is changed to True to indicate that all remaining elements should be added to the prefix
list. Otherwise, the element is added to the suffix list.
rsplit is just a tiny wrapper around rsplit_ that returns a binary tuple with just the prefix and
suffix lists. The rsplit function works as intended:
*Main> rsplit '/' "the/AT"
*Main> rsplit '/' "a/b/TEST"
2This String representation is also used by ghci to print the value of a TrainingInstance.
Part of speech tagging
We are now able to get the necessary data out of a String containing a token and a tag. We can simply
construct a training instance by converting the tuple:
toTrainingInstance :: String -> TrainingInstance
toTrainingInstance s = let (token, tag) = rsplit '/' s in
TrainingInstance token tag
Why not see how we are doing, and get the ten first training instances of the Brown corpus?
*Main> h <- IO.openFile "brown-pos-train.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
*Main> take 10 $ map toTrainingInstance $ words c
[TrainingInstance "The" "AT",TrainingInstance "Fulton" "NP",
TrainingInstance "County" "NN",TrainingInstance "Grand" "JJ",
TrainingInstance "Jury" "NN",TrainingInstance "said" "VBD",
TrainingInstance "Friday" "NR",TrainingInstance "an" "AT",
TrainingInstance "investigation" "NN",TrainingInstance "of" "IN"]
Alright! That's indeed our corpus in beautified format. The next step is to traverse this corpus,
registering for each word with which tag it occurred (and how often). For this we write the
tokenTagFreq function:
import qualified Data.List as L
import qualified Data.Map as M
tokenTagFreqs :: [TrainingInstance] -> M.Map Token (M.Map Tag Int)
tokenTagFreqs = L.foldl' countWord M.empty
countWord m (TrainingInstance token tag) =
M.insertWith (countTag tag) token (M.singleton tag 1) m
countTag tag _ old = M.insertWith
(\newFreq oldFreq -> oldFreq + newFreq) tag 1 old
This function is very comparable to the countElem function we saw earlier, the primary difference
being that we have to handle two levels of maps. Every Token in the first Map is associated with a
value that is itself a Map that maps Tags to frequencies (Int). If we have not seen a particular Token
yet, we will insert it to the map with the Token as the key, the value is a map with just one key/value:
the Tag associated with the token and a frequency of one. If the Token was seen before, we will update
the frequency of the associated Tag, setting it to one, if the Tag was never seen before with this token.
Let us test tokenTagFreqs on the first ten training instances as well:
*Main> h <- IO.openFile "brown-pos-train.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
*Main> tokenTagFreqs $ take 10 $ map toTrainingInstance $ words c
fromList [("County",fromList [("NN",1)]),("Friday",fromList [("NR",1)]),
("Fulton",fromList [("NP",1)]),("Grand",fromList [("JJ",1)]),
("Jury",fromList [("NN",1)]),("The",fromList [("AT",1)]),
("an",fromList [("AT",1)]),("investigation",fromList [("NN",1)]),
("of",fromList [("IN",1)]),("said",fromList [("VBD",1)])]
It seems to work, but we cannot be sure until we have seen duplicates and ambiguous tokens.
You may want to play a little with larger corpus samples or artificial training data to confirm that
tokenTagFreqs works as intended.
The next thing we need for our first part of speech tagger is use the map defined by tokenTagFreqs
to find the most frequent tag for a word. This is a typical mapping situation: for each key/value pair
in the Map, we want to transform its value. The value was a Map, mapping Tag to Int, and we want
the value to be a Tag, namely the most frequent Tag. There is also a map functions for Map:
*Main> :type :: (a -> b) -> M.Map k a -> M.Map k b
Part of speech tagging
57 accepts some function to map every value in a Map to a new value. For getting the
most frequent Tag, we have to fold over the inner map, storing the most frequent tag and its frequency
in the accumulator. The Data.Map module provides the foldlWithKey to fold over keys and values:
*Main> :type Data.Map.foldlWithKey
Data.Map.foldlWithKey :: (b -> k -> a -> b) -> b -> M.Map k a -> b
This looks like the usual suspect, however, the folding function takes an additional parameter. The
folding function has the current accumulator, the current key, and the associated value as its arguments.
Using these building blocks, we can construct the tokenMostFreqTag function:
tokenMostFreqTag :: M.Map Token (M.Map Tag Int) -> M.Map Token Tag
tokenMostFreqTag = mostFreqTag
mostFreqTag = fst . M.foldlWithKey maxTag ("NIL", 0)
maxTag acc@(maxTag, maxFreq) tag freq
| freq > maxFreq = (tag, freq)
| otherwise = acc
The main function body uses mostFreqTag to get the most frequent tag for each token. mostFreqTag
folds over all tokens and frequencies of a map associated with a token. The initial value of the
accumulator is the dummy tag 'NIL'. The maxTag function that is used in the fold will replace the
accumulator with the current tag and its frequency if its frequency is higher than the frequency of the
tag in the accumulator. Otherwise, the tag in the accumulator is more frequent, and the accumulator is
retained. After folding, we have the pair of the most frequent tag, and its frequency. We use the fst
function to get the first element of this pair.
You can craft some examples to check whether tokenMostFreqTag works as intended. For example:
*Main> tokenMostFreqTag $ tokenTagFreqs [TrainingInstance "a" "A",
TrainingInstance "a" "B", TrainingInstance "a" "A"]
fromList [("a","A")]
Combining tokenTagFreqs and tokenMostFreqTag we can make a simple function to train our
first tagging model from a list of TrainingInstance:
trainFreqTagger :: [TrainingInstance] -> M.Map Token Tag
trainFreqTagger = tokenMostFreqTag . tokenTagFreqs
Next up is the actual tagger: it simply looks up a token, returning the most frequent tag of a token. Since
not all tags may be known, you may want to decide how to handle unknown tags. For now, we will
just return the Maybe Tag type, allowing us to return Nothing in the case we do not know how to tag
a word. We will define the function freqTagWord as a simple wrapper around Data.Map.lookup:
freqTagWord :: M.Map Token Tag -> Token -> Maybe Tag
freqTagWord m t = M.lookup w t
We can now train our model from the Brown corpus, and tag some sentences:
*Main> h <- IO.openFile "brown-pos-train.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
*Main> let model = trainFreqTagger $ map toTrainingInstance $ words c
*Main> map (freqTagWord model) ["The","cat","is","on","the","mat","."]
[Just "AT",Just "NN",Just "BEZ",Just "IN",Just "AT",Just "NN",Just "."]
*Main> map (freqTagWord model) ["That's","right",",","the","mascara",
[Just "DT+BEZ",Just "QL",Just ",",Just "AT",Just "NN",Just "NN",Just ".",
Nothing,Just "CC",Nothing,Just ".",Just "RB",Just "AT",Nothing,
Just "NN",Just "."]
Isn't that NLP for the working programmer? Not only did you learn about POS tagging, you built your
own first POS tagger in just a few lines of Haskell code. In the next section we will be a bit more
scientific, and focus on evaluation of taggers.
Part of speech tagging
Now that you wrote your first tagger, the question is how well it work. Not only to show it off to
your colleagues (it will do relatively well), but also to be able to see how future changes impact the
performance of the tagger. To check the performance of the tagger, we will use an evaluation corpus.
You should never evaluate a natural language processing component on the training data, because it is
easy to perform well on seen data. Suppose that you wrote a tagger that just remembered the training
corpus exactly. This tagger would tag every word correctly, but it will behave badly on unseen data.
For evaluating our taggers, we will use another set of sentences from the Brown corpus. These
annotated sentences are provided in brown-pos-test.txt. Since file has the same format as brown-
pos-train.txt, it can also be read as a list of TrainingInstance.
To evaluate a POS tagger, we will write a function that takes a tagging function (Word -> Maybe
Tag) as its first argument and a training corpus as its second argument. It should then return a tuple
with the total number of tokens in the corpus, the number of tokens that were tagged correctly, and
the number of tokens for which the tagger did not provide an analysis (returned Nothing). This is the
evalTagger function:
evalTagger tagFun = L.foldl' eval (0, 0, 0)
eval (n, c, u) (TrainingInstance token correctTag) =
case tagFun token of
Just tag -> if tag == correctTag then
(n+1, c+1, u)
(n+1, c, u)
Nothing -> (n+1, c, u+1)
The function is pretty simple, it folds over all instances in the evaluation data. The counts are
incremented in the following manner:
If the tagger returned a tag for the current token, we have two options:
The tagger picked the correct tag. We increment the number of tokens and the number of correct
tags by one.
The tagger picked an incorrect tag. We only increment the number of tokens by one.
The tagger returned no tag for the current token. We increment the number of tokens and the number
of untagged tokens by one.
Time to evaluate your first tagger!
*Main> h <- IO.openFile "brown-pos-train.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
*Main> let model = trainFreqTagger $ map toTrainingInstance $ words c
*Main> i <- IO.openFile "brown-pos-test.txt" IO.ReadMode
*Main> d <- IO.hGetContents i
*Main> evalTagger (freqTagWord model) $ map toTrainingInstance $ words d
Those are quite impressive numbers for a first try, 239230 / 272901 * 100% = 87.66% of the tokens
were tagged and tagged correctly. Of the remaining 12.34% of the tokens, 11536 / 272901 * 100% =
4.23% of the words were not known. This means that we tagged 239230 / (272901 - 11536) * 100%
= 91.53% of the words known to our model correctly.
To get an impression what these numbers actually mean, we will create a baseline. A baseline is a
dumb model that indicates (more or less) the range we are working in. Our baseline will simply pick
the most frequent tag for every token (as in, most frequent in the corpus, not for the token). We will
generalize the function a bit, allowing us to specify the tag to be used:
Part of speech tagging
baselineTagger :: Tag -> Token -> Maybe Tag
baselineTagger tag _ = Just tag
The most frequent tag in the Brown corpus is NN, the singular common noun. Let's evaluate the
baseline tagger:
*Main> h <- IO.openFile "brown-pos-test.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
*Main> evalTagger (baselineTagger "NN") $ map toTrainingInstance $ words c
We sure do a lot better than this baseline at 31815 / 272901 * 100% = 11.66%! What if we
implement the same heuristic for unknown words in our frequency tagger? You may expect it to
only correct a small proportion of unknown words, but trying never hurts. We add a function named
backOffTagger that wraps a tagger, returning some default tag if the tagger failed to find a tag for
a token:
backoffTagger :: (Token -> Maybe Tag) -> Tag -> Token -> Maybe Tag
backoffTagger f bt t = let pick = f t in
case pick of
Just tag -> Just tag
Nothing -> Just bt
See how we can nicely cascade taggers by writing higher-order functions? We proceed to evaluate
this tagger:
*Main> h <- IO.openFile "brown-pos-train.txt" IO.ReadMode
*Main> c <- IO.hGetContents h
*Main> let model = trainFreqTagger $ map toTrainingInstance $ words c
*Main> i <- IO.openFile "brown-pos-test.txt" IO.ReadMode
*Main> d <- IO.hGetContents i
*Main> evalTagger (backoffTagger (freqTagWord model) "NN") $
map toTrainingInstance $ words d
That did improve performance some. Of the 11536 tokens that we did not tag in the frequency-based
tagger, we now tagged 2360 tokens correctly. This means that we tagged 20.46% of the unknown
words correctly. This is almost double of the baseline, how is that possible? It turns out that of
some classes of tokens, such as articles, prepositions, and tokens, you will never encounter new ones
in unseen data. Unknown words are often nouns, verbs, and adjectives. Since nouns form a larger
proposition of unknown words than all words, you will also get a better performance when guessing
that a word is a singular common noun in unseen data.
Table 7.1, “Performance of the frequency tagger” summarizes the result so far. We have also added
the score of the oracle this is the performance that you would attain if the tagger was omniscient. In
this task, the oracle performs the task perfectly, but this is not true for every task.
Table 7.1. Performance of the frequency tagger
Tagger Accuracy (%)
Baseline 11.66
Frequency-based 87.66
Frequency-based + backoff 88.53
Oracle 100.00
Transformation-based tagging
While the frequency-tagger that you developed over the last two sections was a good first attempt at
POS tagging, the performance of taggers can be improved by taking context into account. To give an
Part of speech tagging
example, the token 'saving' is used as a verb most frequently. However, when the word is used as a
noun, this can often be derived from the context, as in the following two sentences:
1. The/AT weight/NN advantage/NN ,/, plus/CC greater/JJR durability/NN of/IN the/AT plastic/NN
unit/NN ,/, yields/VBZ a/AT saving/NN of/IN about/RB one-fifth/NN in/IN shipping/VBG ./.
2. Its/PP$ elimination/NN would/MD result/VB in/IN the/AT saving/NN of/IN interest/ NN costs/
NNS ,/, heavy/JJ when/WRB short-term/NN money/NN rates/NNS are/BER high /JJ ,/, and/CC
in/IN freedom/NN from/IN dependence/NN on/IN credit/NN which/WDT is/BEZ not/* always/RB
available/JJ when/WRB needed/VBN most/RBT ./.
In both cases, saving is preceded by an article and succeeded by a preposition. The context
disambiguates what specific reading of the token 'saving' should be used.
We could manually inspect all errors in the training corpus after tagging it with the frequency-
based tagger, and write rules that correct mistaggings. This has been done in the past, and can
give a tremendous boost in performance. Unfortunately, finding such rules is very tedious work,
and specific to one language and tag set. Fortunately, [bib-brill1992] has shown that such rules can
be learnt automatically using so-called transformation-based learning. The learning procedure is
1. Tag every token in the training corpus using the most frequent tag for a word.
2. Create rules from rule templates that correct incorrectly tagged words.
3. Count how many corrections were made and errors were introduced when each rule is applied to
the corpus.
4. Select the best rule according to the following equation:
Equation 7.1. Transformation rule selection criterion
5. Go to step 2, unless a threshold has been reached (e.g. rules do not give a net improvement).
The rule templates follow a very simple format. These are two examples from Brill's paper:
1. old_tag new_tag NEXT-TAG tag
2. old_tag new_tag PREV-TAG to
Two possible rules derived from these rule templates are:
The first rule replaces the tag 'TO' (infinitival 'to') by 'IN' (preposition) if the next tag is 'AT' (article).
The second rule, replaces the tag 'NN' (singular common noun) to 'VB' (verb, base) if the previous tag
was 'TO' (infinitival 'to'). As you can immediately see, these are two very effective rules.
Since you already have a frequency-based tagger, you can already perform the first step of the learning
procedure for transformation-based tagging. What we still need are rule templates, rule extractors, and
a scoring function. For brevity, we will only focus on three tag-based templates, but after implementing
the learning procedure, it should be fairly obvious how to had other contexts and integrating words in
templates. The templates that we will create, will take be all variations on directly surrounding tags
(previous tag, next tag, both surrounding tags). Thanks to Haskell's algebraic data types, we can easily
model these templates:
data Replacement = Replacement Tag Tag
deriving (Eq, Ord, Show)
Part of speech tagging
data TransformationRule =
NextTagRule Replacement Tag
| PrevTagRule Replacement Tag
| SurroundTagRule Replacement Tag Tag
deriving (Eq, Ord, Show)
To confirm that these templates are indeed working as expected, we can recreate the rules that were
mentioned earlier:
*Main> NextTagRule (Replacement "TO" "IN") "AT"
NextTagRule (Replacement "TO" "IN") "AT"
*Main> PrevTagRule (Replacement "NN" "VB") "TO"
PrevTagRule (Replacement "NN" "VB") "TO"
Awesome! Now on to rule instantiation. We need to instantiate rules for tags that are incorrect, so
ideally we have the corpus represented as a list of binary tuples, where the first element is the correct
tag, and the second element the tag that is currently assigned by the tagger. For instance:
[("AT","AT"),("NN","VB"),("TO", "TO")]
This can simply be done by using Haskell's zip function, that 'zips' together two lists into one list
of binary tuples:
*Main> :type zip
zip :: [a] -> [b] -> [(a, b)]
*Main> let correct = ["AT","NN","TO"]
*Main> let tagged = ["AT","VB","TO"]
*Main> zip correct tagged
However, using lists is not really practical in this case. By the way they are normally traversed, the
current element is always the head, meaning that we do not readily have access to previous elements.
But we no need to access previous elements for the PrevTagRule and SurroundTagRule templates. We
can write our own function that keeps track of previous elements, but a package with such functionality,
called ListZipper, is already available. After using cabal to install the ListZipper package, you will
have access to the Data.List.Zipper module. A Zipper can be seen as a list that can be traversed in two
directions. We can construct a Zipper from a list:
*Main> let taggingState = Data.List.Zipper.fromList $
zip ["AT","NN","TO"] ["AT","VB","TO"]
*Main> taggingState
Zip [] [("AT","AT"),("NN","VB"),("TO","TO")]
We can get the current element (the element the so-called cursor is pointing at) in the zipper using
*Main> Data.List.Zipper.cursor taggingState
We can move the cursor to the left (point to the previous element) with Data.List.Zipper.left,
and to the right (point to the next element) with Data.List.Zipper.right:
*Main> Data.List.Zipper.right taggingState
Zip [("AT","AT")] [("NN","VB"),("TO","TO")]
*Main> Data.List.Zipper.cursor $ Data.List.Zipper.right taggingState
*Main> Data.List.Zipper.left $ Data.List.Zipper.right $