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A Program for Aligning Sentences in
Bilingual Corpora
William A. Gale*
AT&T Bell Laboratories Kenneth W. Church*
AT&T Bell Laboratories
Researchers in both machine translation (e.g., Brown et al. 1990) and bilingual lexicography (e.g.,
Klavans and Tzoukermann 1990) have recently become interested in studying bilingual corpora,
bodies of text such as the Canadian Hansards (parliamentary proceedings), which are available
in multiple languages (such as French and English). One useful step is to align the sentences,
that is, to identify correspondences between sentences in one language and sentences in the other
language.
This paper will describe a method and a program (align) for aligning sentences based on a
simple statistical model of character lengths. The program uses the fact that longer sentences in
one language tend to be translated into longer sentences in the other language, and that shorter
sentences tend to be translated into shorter sentences. A probabilistic score is assigned to each
proposed correspondence of sen tences, based on the scaled difference of lengths of the two sentences
(in characters) and the variance of this difference. This probabilistic score is used in a dynamic
programming framework to find the maximum likelihood alignment of sentences.
It is remarkable that such a simple approach works as well as it does. An evaluation was
performed based on a trilingual corpus of economic reports issued by the Union Bank of Switzer-
land (UBS) in English, French, and German. The method correctly aligned all but 4% of the
sentences. Moreover, it is possible to extract a large subcorpus that has a much smaller error
rate. By selecting the best-scoring 80% of the alignments, the error rate is reduced from 4% to
0.7%. There were more errors on the English-French subcorpus than on the English-German
subcorpus, showing that error rates will depend on the corpus considered; however, both were
small enough to hope that the method will be useful for many language pairs.
To further research on bilingual corpora, a much larger sample of Canadian Hansards (ap-
proximately 90 million words, half in English and and half in French) has been aligned with the
align
program and will be available through the Data Collection Initiative of the Association
for Computational Linguistics (ACL/DCI). In addition, in order to facilitate replication of the
align
program, an appendix is provided with detailed c-code of the more difficult core of the
align
program.
1. Introduction
Researchers in both machine translation (e.g., Brown et al. 1990) and
bilingual lexi-
cography
(e.g., Klavans and Tzoukermann 1990) have recently become interested in
studying bilingual corpora, bodies of text such as the Canadian Hansards
(parliamen-
tary
debates), which
are available in multiple
languages (such as French and English).
The sentence alignment task is to identify correspondences between sentences in one
* AT&T
Bell
Laboratories 600 Mountain Avenue Murray Hill, NJ, 07974
(~) 1993 Association for Computational Linguistics
Computational Linguistics Volume 19, Number 1
Table 1
Input to alignment program.
English French
According to our survey, 1988 sales of
mineral water and soft drinks were much
higher than in 1987, reflecting the grow-
ing popularity of these products. Cola drink
manufacturers in particular achieved above-
average growth rates. The higher turnover
was largely due to an increase in the sales
volume. Employment and investment levels
also climbed. Following a two-year transi-
tional period, the new Foodstuffs Ordinance
for Mineral Water came into effect on April
1, 1988. Specifically, it contains more strin-
gent requirements regarding quality consis-
tency and purity guarantees.
Quant aux eaux min6rales et aux limonades,
elles rencontrent toujours plus d'adeptes. En
effet, notre sondage fait ressortir des ventes
nettement sup6rieures a celles de 1987, pour
les boissons ~ base de cola notamment. La
progression des chiffres d'affaires r6sulte en
grande partie de l'accroissement du volume
des ventes. Uemploi et les investissements
ont 6galement augment6. La nouvelle ordon-
nance f6d6rale sur les denr6es alimentaires
concernant entre autres les eaux min6rales,
entr6e en vigueur le ler avril 1988 apr6s une
p6riode transitoire de deux ans, exige surtout
une plus grande constance dans la qualit6 et
une garantie de la puret6.
language and sentences in the other language. This task is a first step toward the more
ambitious task finding correspondences among words. 1
The input is a pair of texts such as Table 1. The output identifies the alignment
between sentences. Most English sentences match exactly one French sentence, but it
is possible for an English sentence to match two or more French sentences. The first
two English sentences in Table 2 illustrate a particularly hard case where two English
sentences align to two French sentences. No smaller alignments are possible because
the clause "... sales ...were higher..." in the first English sentence corresponds to
(part of) the second French sentence. The next two alignments below illustrate the
more typical case where one English sentence aligns with exactly one French sentence.
The final alignment matches two English sentences to a single French sentence. These
alignments agreed with the results produced by a human judge.
Aligning sentences is just a first step toward constructing a probabilistic dictionary
(Table 3) for use in aligning words in machine translation (Brown et al. 1990), or for
constructing a bilingual concordance (Table 4) for use in lexicography (Klavans and
Tzoukermann 1990).
Although there has been some previous work on the sentence alignment (e.g.,
Brown, Lai, and Mercer 1991 [at IBM], Kay and R6scheisen [this issue; at Xerox],
and Catizone, Russell, and Warwick, in press [at ISSCO], the alignment task remains a
significant obstacle preventing many potential users from reaping many of the benefits
of bilingual corpora, because the proposed solutions are often unavailable, unreliable,
and/or computationally prohibitive.
Most of the previous work on sentence alignment has yet to be published. Kay's
draft (Kay and R6scheisen; this issue), for example, was written more than two years
ago and is still unpublished. Similarly the IBM work is also several years old, but not
1 In statistics, string-matching problems are divided into two classes:
alignment
problems and
correspondence
problems. Crossing dependencies are possible in the latter, but not in the former.
76
William A. Gale and Kenneth W. Church Program for Aligning Sentences
Table 2
Output from alignment program.
English French
According to our survey, 1988 sales of min-
eral water and soft drinks were much higher
than in 1987, reflecting the growing popular-
ity of these products. Cola drink manufac-
turers in particular achieved above-average
growth rates.
Quant aux eaux min6rales et aux limonades,
elles rencontrent toujours plus d'adeptes. En
effet, notre sondage fait ressortir des ventes
nettement sup6rieures a celles de 1987, pour
les boissons a base de cola notamment.
The higher turnover was largely due to an La progression des chiffres d'affaires r6sulte
increase in the sales volume, en grande partie de l'accroissement du vol-
ume des ventes.
Employment and investment levels also L'emploi et les investissements ont 6gale-
climbed, ment augment6.
Following a two-year transitional period, the
new Foodstuffs Ordinance for Mineral Wa-
ter came into effect on April 1, 1988. Specif-
ically, it contains more stringent require-
ments regarding quality consistency and pu-
rity guarantees.
La nouvelle ordonnance f6d6rale sur les
denr6es alimentaires concernant entre autres
les eaux min6rales, entr6e en vigueur le ler
avril 1988 apr6s une p6riode transitoire de
deux ans, exige surtout une plus grande con-
stance dans la qualit6 et une garantie de la
puret6.
Table 3
An entry in a probabilistic dictionary.
(from Brown et al. 1990)
English French Prob (FrenchlEnglish)
the le 0.610
the la 0.178
the 1' 0.083
the les 0.023
the ce 0.013
the il 0.012
the de 0.009
the ~ 0.007
the que 0.007
very well documented in the published literature; consequently, there has been a lot
of unnecessary subsequent work at ISSCO and elsewhere. 2
The method we describe has the same sentenceqength basis as does that of Brown,
Lai, and Mercer, while the two differ considerably from the lexical approaches tried
by Kay and R6scheisen and by Catizone, Russell, and Warwick.
The feasibility of other methods has varied greatly. Kay's approach is apparently
quite slow. At least, with the currently inefficient implementation, it might take hours
2 After we finished most of this work, it came to our attention that the IBM MT group has at least four
papers that mention sentence alignment. (Brown et al. 1988a,b) start from a set of aligned sentences,
suggesting that they had a solution to the sentence alignment problem back in 1988. Brown et al. (1990)
mention that sentence lengths formed the basis of their method. The draft by Brown, Lai, and Mercer
(1991) describes their process without giving equations.
77
Computational Linguistics Volume 19, Number 1
Table 4
A bilingual concordance.
bank/banque ("money" sense)
it could also be a place where we would have a bank of experts. SENT i know several people who a
ftre le lieu oti se retrouverait une esp6ce de banque d' experts. SENT je connais plusieurs pers
f finance (mr. wilson) and the governor of the
es finances ( m. wilson ) et le gouverneur de la
reduced by over 800 per cent in one week through
us de 800 p. 100 en une semaine a cause d'une
bank of canada have frequently on behalf of the ca
banque du canada ont fr6quemment utilis6 au co
bank action. SENT there was a haberdasher who wou
banque. SENT voila un chemisier qui aurait appr
bank/banc ("place" sense)
h a forum. SENT such was the case in the georges
entre les 6tats-unis et le canada a propos du
han i did. SENT he said the nose and tail of the
gouvernement avait c6d6 les extr6mit6s du
he fishing privileges on the nose and tail of the
les privil6ges de p~che aux extr6mit6s du
bank issue which was settled between canada and th
banc de george. SENT c'est dans le but de r6
bank were surrendered by this government. SENT th
banc. SENT en fait, lors des n6gociations de 1
bank went down the tube before we even negotiated
banc ont 6t6 liquid6s avant rhyme qu' on ai
to align a single Scientific American article (Kay, personal communication). It ought to
be possible to achieve fairly reasonable results with much less computation. The IBM
algorithm is much more efficient since they were able to extract nearly 3 million pairs
of sentences from Hansard materials in 10 days of running time on an IBM Model
3090 mainframe computer with access to 16 megabytes of virtual memory (Brown,
Lai, and Mercer 1991).
The evaluation of results has been absent or rudimentary. Kay gives positive ex-
amples of the alignment process, but no counts of error rates. Brown, Lai, and Mercer
(1991) report that they achieve a 0.6% error rate when the algorithm suggests aligning
one sentence with one sentence. However, they do not characterize its performance
overall or on the more difficult cases.
Since the research community has not had access to a practical sentence alignment
program, we thought that it would be helpful to describe such a program
(align)
and to
evaluate its results. In addition, a large sample of Canadian Hansards (approximately
90 million words, half in French and half in English) has been aligned with the
align
program and has been made available to the general research community through the
Data Collection Initiative of the Association for Computational Linguistics (ACL/DCI).
In order to facilitate replication of the
align
program, an appendix is provided with
detailed c-code of the more difficult core of the
align
program.
The
align
program is based on a very simple statistical model of character lengths.
The model makes use of the fact that longer sentences in one language tend to be
translated into longer sentences in the other language, and that shorter sentences
tend to be translated into shorter sentences. A probabilistic score is assigned to each
pair of proposed sentence pairs, based on the ratio of lengths of the two sentences
(in characters) and the variance of this ratio. This probabilistic score is used in a
dynamic programming framework in order to find the maximum likelihood alignment
of sentences.
It is remarkable that such a simple approach can work as well as it does. An
evaluation was performed based on a trilingual corpus of 15 economic reports issued
by the Union Bank of Switzerland (UBS) in English, French, and German (14,680
words, 725 sentences, and 188 paragraphs in English and corresponding numbers in
78
William A. Gale and Kenneth W. Church Program for Aligning Sentences
the other two languages). The method correctly aligned all but 4% of the sentences.
Moreover, it is possible to extract a large subcorpus that has a much smaller error
rate. By selecting the best-scoring 80% of the alignments, the error rate is reduced
from 4% to 0.7%. There were more errors on the English-French subcorpus than on
the English-German subcorpus, showing that error rates will depend on the corpus
considered; however, both were small enough for us to hope that the method will be
useful for many language pairs. We believe that the error rate is considerably lower
in the Canadian Hansards because the translations are more literal.
2. Paragraph Alignment
The sentence alignment program is a two-step process. First paragraphs are aligned,
and then sentences within a paragraph are aligned. It is fairly easy to align paragraphs
in our trilingual corpus of Swiss banking reports since the boundaries are usually
clearly marked. However, there are some short headings and signatures that can be
confused with paragraphs. Moreover, these short "pseudo-paragraphs" are not always
translated into all languages. On a corpus this small the paragraphs could have been
aligned by hand. It turns out that "pseudo-paragraphs" usually have fewer than 50
characters and that real paragraphs usually have more than 100 characters. We used
this fact to align the paragraphs automatically, checking the result by hand.
The procedure correctly aligned all of the English and German paragraphs. How-
ever, one of the French documents was badly translated and could not be aligned
because of the omission of one long paragraph and the duplication of a short one.
This document was excluded for the purposes of the remainder of this experiment.
We will show below that paragraph alignment is an important step, so it is fortu-
nate that it is not particularly difficult. In aligning the Hansards, we found that para-
graphs were often already aligned. For robustness, we decided to align paragraphs
within certain fairly reliable regions (denoted by certain Hansard-specific formatting
conventions) using the same method as that described below for aligning sentences
within each paragraph.
3. A Dynamic Programming Framework
Now, let us consider how sentences can be aligned within a paragraph. The program
makes use of the fact that longer sentences in one language tend to be translated into
longer sentences in the other language, and that shorter sentences tend to be trans-
lated into shorter sentences. 3 A probabilistic score is assigned to each proposed pair
of sentences, based on the ratio of lengths of the two sentences (in characters) and
the variance of this ratio. This probabilistic score is used in a dynamic programming
framework in order to find the maximum likelihood alignment of sentences. The
fol-
3 We will have little to say about how sentence boundaries are identified. Identifying sentence
boundaries is not always as easy as it might appear for reasons described in Liberman and Church (in
press). It would be much easier if periods were always used to mark sentence boundaries; but
unfortunately, many periods have other purposes. In the Brown Corpus, for example, only 90% of the
periods are used to mark sentence boundaries; the remaining 10% appear in numerical expressions,
abbreviations, and so forth. In the
Wall Street Journal,
there is even more discussion of dollar amounts
and percentages, as well as more use of abbreviated titles such as
Mr.;
consequently, only 53% of the
periods in the
Wall Street Journal
are used to identify sentence boundaries. For the UBS data, a simple
set of heuristics were used to identify sentences boundaries. The dataset was sufficiently small that it
was possible to correct the remaining mistakes by hand. For a larger dataset, such as the Canadian
Hansards, it was not possible to check the results by hand. We used the same procedure that is used in
Church (1988). This procedure was developed by Kathryn Baker (unpublished).
79
Computational Linguistics Volume 19, Number 1
f.-
e-
f-
Q.
t~
e~
¢-
E
0
0
0
0
P,
o Ol
I
0
*¢**.,
*
f***
. .,***
** t- **
I I I
500 1000 1500
English paragraph length
Figure 1
Paragraph lengths are highly correlated. The horizontal axis shows the length of English
paragraphs, while the vertical scale shows the lengths of the corresponding German
paragraphs. Note that the correlation is quite large (.991).
lowing striking figure could easily lead one to this approach. Figure 1 shows that the
lengths (in characters) of English and German paragraphs are highly correlated (.991).
Dynamic programming is often used to align two sequences of symbols in a vari-
ety of settings, such as genetic code sequences from different species, speech sequences
from different speakers, gas chromatograph sequences from different compounds, and
geologic sequences from different locations (Sankoff and Kruskal 1983). We could ex-
pect these matching techniques to be useful, as long as the order of the sentences does
not differ too radically between the two languages. Details of the alignment techniques
differ considerably from one application to another, but all use a distance measure to
compare two individual elements within the sequences and a dynamic programming
algorithm to minimize the total distances between aligned elements within two se-
quences. We have found that the sentence alignment problem fits fairly well into this
framework, though it is necessary to introduce a fairly interesting innovation into the
structure of the distance measure.
Kruskal and Liberman (1983) describe distance measures as belonging to one of
two classes:
trace
and
time-warp.
The difference becomes important when a single
element of one sequence is being matched with multiple elements from the other. In
trace applications, such as genetic code matching, the single element is matched with
just one of the multiple elements, and all of the others will be ignored. In contrast,
in time-warp applications such as speech template matching, the single element is
matched with each of the multiple elements, and the single element will be used
in multiple matches. Interestingly enough, our application does not fit into either of
80
William A. Gale and Kenneth W. Church Program for Aligning Sentences
"0
0
tO
0
0
d
0
0
d
] I I I I [ ]
-3 -2 -1 0 1 2 3
delta
Figure 2
Delta is approximately normal. The horizontal axis shows ~, while the vertical scale shows the
empirical density of delta for the hand-aligned regions as points, and a normal (0,1) density
plot (lines) for comparison• The empirical density is slightly more peaked than normal (and its
mean is not quite zero), but the differences are small enough for the purposes of the algorithm.
Kruskal and Liberman's classes because our distance measure needs to compare the
single element with an aggregate of the multiple elements.
4. The Distance Measure
It is convenient for the distance measure to be based on a probabilistic model so that
information can be combined in a consistent way. Our distance measure is an estimate
of -logProb(match
I
6), where ~ depends on 11 and/2, the lengths of the two portions
of text under consideration. The log is introduced here so that adding distances will
produce desirable results.
This distance measure is based on the assumption that each character in one lan-
guage, L~, gives rise to a random number of characters in the other language, L2. We
assume these random variables are independent and identically distributed with a
normal distribution. The model is then specified by the mean, c, and variance, s 2, of
this distribution, c is the expected number of characters in L2 per character in Lb and
s 2 is the variance of the number of characters in L2 per character in L1. We define ~ to
be (12 -
llC)/V~l s2
so that it has a normal distribution with mean zero and variance
one (at least when the two portions of text under consideration actually do happen to
be translations of one another).
Figure 2 is a check on the assumption that 6 is normally distributed. The figure is
constructed using the parameters c and s 2 estimated for the program.
81
Computational Linguistics Volume 19, Number 1
0
~ eJ
"0
t'-
Q.
0
I I b I
0 500 1000 1500
English paragraph length
Figure 3
Variance is modeled proportional to length. The horizontal axis plots the length of English
paragraphs, while the vertical axis shows the square of the difference of English and German
lengths, an estimate of variance. The plot indicates that variance increases with length, as
predicted by the model. The line shows the result of a robust regression analysis. Five extreme
points lying above the top of this figure have been suppressed since they did not contribute to
the robust regression.
The parameters c and
S 2 are
determined empirically from the UBS data. We could
estimate c by counting the number of characters in German paragraphs then divid-
ing by the number of characters in corresponding English paragraphs. We obtain
81105/73481 ~ 1.1. The same calculation on French and English paragraphs yields
c ~ 72302/68450 ~ 1.06 as the expected number of French characters per English
character. As will be explained later, performance does not seem to be very sensitive
to these precise language-dependent quantities, and therefore we simply assume the
language-independent value c ~ 1, which simplifies the program considerably. This
value would clearly be inappropriate for English-Chinese alignment, but it seems
likely to be useful for most pairs of European languages.
s 2 is estimated from Figure 3. The model assumes that s 2 is proportional to length.
The constant of proportionality is determined by the slope of the robust regression
line shown in the figure. The result for English--German is s 2 = 7.3, and for English-
French is s 2 = 5.6. Again, we will see that the difference in the two slopes is not
too important. Therefore, we can combine the data across languages, and adopt the
simpler language-independent estimate s 2 ~ 6.8, which is what is actually used in the
program.
We now appeal to Bayes Theorem to estimate
Prob(match ] 6)
as a constant times
Prob(6 I match) Prob(match).
The constant can be ignored since it will be the same for
82
William A. Gale and Kenneth W. Church Program for Aligning Sentences
Table 5
Prob(match)
Category Frequency Prob(match)
1-1 1167 0.89
1-0 or 0-1 13 0.0099
2-1 or 1-2 117 0.089
2-2 15 0.011
1312 1.00
all proposed matches. The conditional probability
Prob(~ I match)
can be estimated by
Prob(~ ] match)
= 2(1 -
Prob(]~l) )
where
Prob(]61)
is the probability that a random variable, z, with a standardized (mean
zero, variance one) normal distribution, has magnitude at least as large as 16]. That is,
Prob(~)- 1 f~
v~ oo e -z2/2
dz.
The program computes 6 directly from the lengths of the two portions of text, 11 and
12, and the two parameters, c and s 2. That is, 6 = (/2 -
llC)/IX/~lS 2.
Then,
Prob(]6])
is
computed by integrating a standard normal distribution (with mean zero and variance
one). Many statistics textbooks include a table for computing this. The code in the
appendix uses the
pnorm
function, which is based on an approximation described by
Abramowitz and Stegun (1964; p. 932, equation 26.2.17).
The prior probability of a match,
Prob(match),
is fit with the values in Table 5, which
were determined from the hand-marked UBS data. We have found that a sentence in
one language normally matches exactly one sentence in the other language (1-1). Three
additional possibilities are also considered: 1-0 (including 0-1), 2-1 (including 1-2), and
2-2. Table 5 shows all four possibilities.
This completes the discussion of the distance measure.
Prob(match I 6)
is computed
as an (irrelevant) constant times
Prob(~ ] match)Prob(match). Prob(match)
is computed
using the values in Table 5.
Prob(6 ] match)
is computed by assuming that
Prob(6 ]
match)
= 2(1 -Prob(]~])
),
where
Prob(16])
has a standard normal distribution. We first
calculate 6 as (12 -
llc)/Ix/~lS 2
and then
Prob(]6[)
is computed by integrating a standard
normal distribution. See the c-function
two_side_distance
in the appendix for an example
of a c-code implementation of these calculations.
The distance function d, represented in the program as
two,side_distance,
is defined
in a general way to allow for insertions, deletion, substitution, etc. The function takes
four arguments: Xl~ Yl, x2, y2.
1. Let
d(xl,yl,
0~ 0) be the cost of substituting Xl with yl,
2. d(xl,
0; 0, 0) be the cost of deleting Xl,
3. d(O, yl;
0, 0) be the cost of insertion of Yl,
4. d(Xl,yl;
x2, 0) be the cost of contracting Xl and x2 to yl,
83
Computational Linguistics Volume 19, Number 1
5. d(Xl,yl;O, y2)
be the cost of expanding X1 to yl and y2, and
6. d(Xl,yl;x2,y2)
be the cost of merging xl and x2 and matching with Yl
and yR.
5. The Dynamic Programming Algorithm
The algorithm is summarized in the following recursion equation. Let
si, i = 1 ... I,
be
the sentences of one language, and
tj,
j -- 1-.. J, be the translations of those sentences in
the other language. Let d be the distance function described in the previous section, and
let
D(i,j)
be the minimum distance between sentences
sl,...si
and their translations
tl,...tj,
under the maximum likelihood alignment.
D(i,j)
is computed by minimizing
over six cases (substitution, deletion, insertion, contraction, expansion, and merger)
which, in effect, impose a set of slope constraints. That is,
D(i,j)
is defined by the
following recurrence with the initial condition
D(i,j) = O.
D(i,j -
1) +
d(O, tj;O,O)
D(i-
1,j) +
d(si, O;O,O)
D(i- 1,j-
1) +
d(si, tj;O,O)
D(i,j) = min D(i- l,j- 2) q- d(si, tj;O, tj_l)
D(i- 2,j-1) + d(si, ty;Si-l,0)
D(i- 2,j- 2) + d(si, tfisi-l,tj-1)
6. Evaluation
To evaluate
align,
its results were compared with a human alignment. All of the UBS
sentences were aligned by a primary judge, a native speaker of English with a reading
knowledge of French and German. Two additional judges, a native speaker of French
and a native speaker of German, respectively, were used to check the primary judge on
43 of the more difficult paragraphs having 230 sentences (out of 118 total paragraphs
with 725 sentences). Both of the additional judges were also fluent in English, having
spent the last few years living and working in the United States, though they were
both more comfortable with their native language than with English.
The materials were prepared in order to make the task somewhat less tedious for
the judges. Each paragraph was printed in three columns, one for each of the three
languages: English, French, and German. Blank lines were inserted between sentences.
The judges were asked to draw lines between matching sentences. The judges were
also permitted to draw a line between a sentence and "null" if they thought that the
sentence was not translated. For the purposes of this evaluation, two sentences were
defined to "match" if they shared a common clause. (In a few cases, a pair of sentences
shared only a phrase or a word, rather than a clause; these sentences did not count as
a "match" for the purposes of this experiment.)
After checking the primary judge with the other two judges, it was decided that
the primary judge's results were sufficiently reliable that they could be used as a
standard for evaluating the program. The primary judge made only two mistakes on
the 43 hard paragraphs (one French mistake and one German mistake), whereas the
program made 44 errors on the same materials. Since the primary judge's error rate is
so much lower than that of the program, it was decided that we needn't be concerned
with the primary judge's error rate. If the program and the judge disagree, we can
assume that the program is probably wrong.
The 43 "hard" paragraphs were selected by looking for sentences that mapped
to something other than themselves after going through both German and French.
84
William A. Gale and Kenneth W. Church Program for Aligning Sentences
Table 6
Complex matches are more difficult.
category English-French English-German total
N err % N err % N err %
1-0
1-1
2-1
2-2
3-1
3-2
8 8 100
542 14 2.6
59 8 14
9 3 33
1 1
100
1 1 100
5 5 100
625 9 1.4
58 2 3.4
6 2 33
1 1 100
0 0 --
13 13 100
1167 23 2.0
117 10 9
15 5 33
2 2 100
1 1
100
Specifically, for each English sentence, we attempted to find the corresponding German
sentences, and then for each of them, we attempted to find the corresponding French
sentences, and then we attempted to find the corresponding English sentences, which
should hopefully get us back to where we started. The 43 paragraphs included all
sentences in which this process could not be completed around the loop. This relatively
small group of paragraphs (23% of all paragraphs) contained a relatively large fraction
of the program's errors (82%). Thus, there seems to be some verification that this
trilingual criterion does in fact succeed in distinguishing more difficult paragraphs
from less difficult ones.
There are three pairs of languages: English-German, English-French, and French-
German. We will report on just the first two. (The third pair is probably dependent
on the first two.) Errors are reported with respect to the judge's responses. That is,
for each of the "matches" that the primary judge found, we report the program as
correct if it found the "match" and incorrect if it didn't. This procedure is better than
comparing on the basis of alignments proposed by the algorithm for two reasons.
First, it makes the trial "blind," that is, the judge does not know the algorithm's result
when judging. Second, it allows comparison of results for different algorithms on a
common basis.
The program made 36 errors out of 621 total alignments (5.8%) for English-French
and 19 errors out of 695 (2.7%) alignments for English-German. Overall, there were
55 errors out of a total of 1316 alignments (4.2%). The higher error rate for English-
French alignments may result from the German being the original, so that the English
and German differ by one translation, while the English and French differ by two
translations.
Table 6 breaks down the errors by category, illustrating that complex matches
are more difficult. 1-1 alignments are by far the easiest. The 2-1 alignments, which
come next, have four times the error rate for 1-1. The 2-2 alignments are harder still,
but a majority of the alignments are found. The 3-1 and 3-2 alignments are not even
considered by the algorithm, so naturally all three instances of these are counted as
errors. The most embarrassing category is 1-0, which was never handled correctly. In
addition, when the algorithm assigns a sentence to the 1-0 category, it is also always
wrong. Clearly, more work is needed to deal with the 1-0 category. It may be necessary
to consider language-specific methods in order to deal adequately with this case.
Since the algorithm achieves substantially better performance on the 1-1 regions,
one interpretation of these results is that the overall low error rate is due to the
high frequency of 1-1 alignments in English-French and English-German translations.
85
Computational Linguistics Volume 19, Number 1
Table 7
The distance measure is the best predictor of errors.
Variable Coef. Std. Dev.
Distance Measure .071 .011
Category Type .52 .47
Paragraph Length .0003 .0005
Sentence Length .0013 .0029
Coef./Std. Dev.
6.5
1.1
0.6
0.5
Translations to linguistically more different languages, such as Hebrew or Japanese,
might encounter a higher proportion of hard matches.
We investigated the possible dependence of the error rate on four variables:
1. Sentence Length
2. Paragraph Length
3. Category Type
4. Distance Measure.
We used logistic regression (Hosmer and Lemeshow 1989) to see how well each of
the four variables predicted the errors. The coefficients and their standard deviations
are shown in Table 7. Apparently, the distance measure is the most useful predictor,
as indicated by the last column. In fact, none of the other three factors was found to
contribute significantly beyond the effect of the distance measure, indicating that the
distance measure is already doing an excellent job, and we should not expect much
improvement if we were to try to augment the measure to take these additional factors
into account.
The fact that the score is such a good predictor of performance can be used to ex-
tract a large subcorpus that has a much smaller error rate. By selecting the best scoring
80% of the alignments, the error rate can be reduced from 4% to 0.7%. In general, we
can trade off the size of the subcorpus and the accuracy by setting a threshold, and
rejecting alignments with a score above this threshold. Figure 4 examines this trade-off
in more detail.
Less formal tests of the error rate in the Hansards suggest that the overall error
rate is about 2%, while the error rate for the easy 80% of the sentences is about 0.4%.
Apparently the Hansard translations are more literal than the UBS reports. It took
20 hours of real time on a sun 4 to align 367 days of Hansards, or 3.3 minutes per
Hansard-day. The 367 days of Hansards contained about 890,000 sentences or about 37
million "words" (tokens). About half of the computer time is spent identifying tokens,
sentences, and paragraphs, and about half of the time is spent in the
align
program
itself.
The overall error, 4.2%, that we get on the UBS corpus is considerably higher
than the 0.6% error reported by Brown, Lai, and Mercer (1991). However, a direct
comparison is misleading because of the differences in corpora and the differences in
sampling. We have observed that the Hansards are much easier than the UBS. Our
error rate drops by about 50% in that case. Aligning the UBS French and English texts is
more difficult than aligning the English and German, because the French and English
86
William A. Gale and Kenneth W. Church Program for Aligning Sentences
03
o
.~, 04
c-
O
0..
0
............. ~_ .......................................................................
I I I I I
20 40 60 80 1 O0
percent of retained alignments
Figure 4
Extracting a subcorpus with lower error rate. The fact that the score is such a good predictor
of performance can be used to extract a large subcorpus that has a much smaller error rate. In
general, we can trade off the size of the subcorpus and the accuracy by setting a threshold and
rejecting alignments with a score above this threshold. The horizontal axis shows the size of
the subcorpus, and the vertical axis shows the corresponding error rate. An error rate of about
2/3% can be obtained by selecting a threshold that would retain approximately 80% of the
corpus.
versions are separated by two translations, both being translations of the German
original. In addition, IBM samples only the 1-1 alignments, which are much easier
than any other category, as one can see from Table 6.
Given these differences in testing methodology as well as the differences in the
algorithms, we find the methods giving broadly similar results. Both methods give
results with sufficient accuracy to use the resulting alignments, or selected portions
thereof, for acquisition of lexical information. And neither method achieves human
accuracy on the task. (Note that one difference between their method and ours is that
they never find 2-2 alignments. This would give their method a minimum overall error
rate of 1.4% on the UBS corpus, three times the human error rate on hard paragraphs.)
We conclude that a sentence alignment method that achieves human accuracy will
need to have lexical information available to it.
7. Variations and Extensions
7.1 Measuring Length in Terms Of Words Rather than Characters
It is interesting to consider what happens if we change our definition of length to count
words rather than characters. It might seem that a word is a more natural linguistic
unit than a character. However, we have found that words do not perform as well as
87
Computational Linguistics Volume 19, Number 1
characters. In fact, the "words" variation increases the number of errors dramatically
(from 36 to 50 for English-French and from 19 to 35 for English-German). The total
errors were thereby increased from 55 to 85, or from 4.2% to 6.5%.
We believe that characters are better because there are more of them, and there-
fore there is less uncertainty. On the average, there are 117 characters per sentence
(including white space) and only 17 words per sentence. Recall that we have modeled
variance as proportional to sentence length,
V(I)
= s21. Using the character data, we
found previously that s 2 ~ 6.5. The same argument applied to words yields s 2 ~ 1.9.
For comparison's sake, it is useful to consider the ratio of
x/-V~/m
(or equivalently,
s/x/-m),
where m is the mean sentence length. We obtain
x/V(m)/m
ratios of 0.22 for
characters and 0.33 for words, indicating that characters are less noisy than words,
and are therefore more suitable for use in
align.
Although Brown, Lai, and Mercer (1991) used lengths measured in words, com-
parisons of error rates between our work and theirs will not test whether characters
or words are more useful. As set out in the previous section, there are numerous
differences in testing methodology and materials. Furthermore, there are apparently
many differences between the IBM algorithm and ours other than the units of mea-
surement, which could also account for any difference on performance. Appropriate
methodology is to compare methods with only one factor varying, as we do here.
7.2 Ignoring Paragraph Boundaries
Recall that
align
is a two-step process. First, paragraph boundaries are identified and
then sentences are aligned within paragraphs. We considered eliminating the first step
and found a threefold degradation in performance. The English-French errors were
increased from 36 to 84, and the English-German errors from 19 to 86. The overall
errors were increased from 55 to 170. Thus the two-step approach reduces errors by
a factor of three. It is possible that performance might be improved further still by
introducing additional alignment steps at the clause and/or phrase levels, but testing
this hypothesis would require access to robust parsing technology.
7.3 Adding a 2-2 Category
The original version of the program did not consider the category of 2-2 alignments.
Table 6 shows that the program was right on 10 of 15 actual 2-2 alignments. This was
achieved at the cost of introducing 2 spurious 2-2 alignments. Thus in 12 tries, the
program was right 10 times, wrong 2 times. This is significantly better than chance,
since there is less than 1% chance of getting 10 or more heads out of 12 flips of a fair
coin. Thus it is worthwhile to include the 2-2 alignment possibility.
7.4 Using More Accurate Parameter Estimates
When we discussed the estimation of the model parameters, c and
s 2,
we mentioned
that it is possible to fit the parameters more accurately if we estimate different values
for each language pair, but that doing so did not seem to increase performance by very
much. In fact, we found exactly the same total number of errors, although the errors are
slightly different. Changing the parameters resulted in four changes to the output for
English-French (two right and two wrong), and two changes to the output for English-
German (one right and one wrong). Since it is more convenient to use language-
independent parameter values, and doing so doesn't seem to hurt performance very
much (if at all), we have decided to adopt the language-independent values.
88
William A. Gale and Kenneth W. Church Program for Aligning Sentences
7.5 Extensions
7.5.1
Hard and Soft Boundaries.
Recall that we rejected one of the French documents
because one paragraph was omitted and two paragraphs were duplicated. We could
have handled this case if we had employed a more powerful paragraph alignment
algorithm. In fact, in aligning the Canadian Hansards, we found that it was necessary
to do something more elaborate than we did for the UBS data. We decided to use
more or less the same procedure for aligning paragraphs within a document as the
procedure that we used for aligning sentences within a paragraph. Let us introduce
the distinction between hard and soft delimiters. The alignment program is defined
to move soft delimiters as necessary within the constraints of the hard delimiters.
Hard delimiters cannot be modified, and there must be equal numbers of them. When
aligning sentences within a paragraph, the program considers paragraph boundaries
to be "hard" and sentence boundaries to be "soft." When aligning paragraphs within
a document, the program considers document boundaries to be "hard" and paragraph
boundaries to be "soft." This entension has been incorporated into the implementation
presented in the appendix.
7.5.2
Augmenting the Dictionary Function to Consider Words.
Many alternative
alignment procedures such as Kay and R6scheisen (unpublished) make use of words. It
ought to help to know that the English string "house" and the French string "maison"
are likely to correspond. Dates and numbers are perhaps an even more extreme exam-
ple. It really ought to help to know that the English string "1988" and the French string
"1988" are likely to correspond. We are currently exploring ways to integrate these
kinds of clues into the framework described above. However, at present, the algorithm
does not have access to lexical constraints, which are clearly very important. We expect
that once these clues are properly integrated, the program will achieve performance
comparable to that of the primary judge. However, we are still not convinced that it
is necessary to process these lexical clues, since the current performance is sufficient
for many applications, such as building a probabilistic dictionary. It is remarkable just
how well we can do without lexical constraints. Adding lexical constraints might slow
down the program and make it less useful as a first pass.
8.
Conclusions
This paper has proposed a method for aligning sentences in a bilingual corpus, based
on a simple probabilistic model, described in Section 3. The model was motivated
by the observation that longer regions of text tend to have longer translations, and
that shorter regions of text tend to have shorter translations. In particular, we found
that the correlation between the length of a paragraph in characters and the length of
its translation was extremely high (0.991). This high correlation suggests that length
might be a strong clue for sentence alignment.
Although this method is extremely simple, it is also quite accurate. Overall, there
was a 4.2% error rate on 1316 alignments, averaged over both English-French and
English-German data. In addition, we find that the probability score is a good predictor
of accuracy, and consequently, it is possible to select a subset of 80% of the alignments
with a much smaller error rate of only 0.7%.
The method is also fairly language-independent. Both English-French and English-
German data were processed using the same parameters. If necessary, it is possible to
fit the six parameters in the model with language-specific values, though, thus far, we
have not found it necessary to do so.
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Computational Linguistics Volume 19, Number 1
We have examined a number of variations. In particular, we found that it is better
to use characters rather than words in counting sentence length. Apparently, the per-
formance is better with characters because there is less variability in the differences of
sentence lengths so measured. Using words as units increases the error rate by hall
from 4.2% to 6.5%.
In the future, we would hope to extend the method to make use of lexical con-
straints. However, it is remarkable just how well we can do without such constraints.
We might advocate our simple character alignment procedure as a first pass, even to
those who advocate the use of lexical constraints. Our procedure would complement
a lexical approach quite well. Our method is quick but makes a few percent errors;
a lexical approach is probably slower, though possibly more accurate. One might go
with our approach when the scores are small, and back off to a lexical-based approach
as necessary.
Acknowledgments
We thank Susanne Wolff and Evelyne
Tzoukermann for their pains in aligning
sentences. Susan Warwick provided us with
the UBS trilingual corpus and convinced us
to work on the sentence alignment problem.
References
Abramowitz, M., and Stegun, I. (1964).
Handbook of Mathematical Functions.
US
Government Printing Office.
Brown, P.; Cocke, J.; Della Pietra, S.; Della
Pietra, V.; Jelinek, F.; Mercer, R.; and
Roossin, P. (1988a). "A statistical
approach to French/English translation."
In
Proceedings, RIA088 Conference.
Cambridge, MA.
Brown, P.; Cocke, J.; Della Pietra, S.; Della
Pietra, V.; Jelinek, F.; Mercer, R.; and
Roossin, P. (1988b). "A statistical
approach to language translation." In
Proceedings, 13th International Conference on
Computational Linguistics (COLING-88).
Budapest, Hungary.
Brown, P.; Cocke, J.; Della Pietra, S.; Della
Pietra, V.; Jelinek, F.; Lafferty, J.; Mercer,
R.; and Roossin, P. (1990). "A statistical
approach to machine translation."
Computational Linguistics,
16, 79-85.
Brown, P.; Lai, J.; and Mercer, R. (1991).
"Aligning sentences in parallel corpora."
In
Proceedings, 47th Annual Meeting of the
Association for Computational Linguistics.
Catizone, R.; Russell, G.; and Warwick, S.
(in press). "Deriving translation data from
bilingual texts." In
Lexical Acquisition:
Using on-line Resources to Build a Lexicon,
edited by Zernik. Lawrence Erlbaum.
Church, K. (1988). "A stochastic parts
program and noun phrase parser for
unrestricted text." In
Proceedings, Second
Conference on Applied Natural Language
Processing.
Austin, TX.
Hosmer, D., and Lemeshow, S. (1989).
Applied Logistic Regression.
Wiley.
Klavans, J., and Tzoukermann, E. (1990).
"The BICORD system." In
Proceedings,
15th International Conference on
Computational Linguistics (COLING-90),
174-179.
Kay, M., and R6scheisen, M. (1988).
"Text-translation alignment." Xerox Palo
Alto Research Center.
Kruskal, J., and Liberman, M. (1983). "The
symmetric time-warping problem: From
continuous to discrete." In
Time Warps,
String Edits, and Macro Molecules: The
Theory and Practice of Sequence Comparison,
edited by D. Sankoff and J. Kruskal.
Addison-Wesley.
Liberman, M., and Church, K. (in press).
"Text analysis and word pronunciation in
text-to-speech synthesis." In
Advances in
Speech Signal Processing,
edited by S. Furui
and M. Sondhi.
Sankoff, D., and Kruskal, J. (1983).
Time
Warps, String Edits, and Macromolecules: The
Theory and Practice of Sequence Comparison.
Addison-Wesley.
90
William A. Gale and Kenneth W. Church Program for Aligning Sentences
Appendix: Program
with Michael D. Riley
The following code is the core of
align.
It is a C language program that inputs two
text files, with one token (word) per line. The text files contain a number of delimiter
tokens. There are two types of delimiter tokens: "hard" and "soft." The hard regions
(e.g., paragraphs) may not be changed, and there must be equal numbers of them in
the two input files. The soft regions (e.g., sentences) may be deleted (1-0), inserted (0-
1), substituted (1-1), contracted (2-1), expanded (1-2), or merged (2-2) as necessary so
that the output ends up with the same number of soft regions. The program generates
two output files. The two output files contain an equal number of soft regions, each
on a line. If the -v command line option is included, each soft region is preceded by
its probability score.
#include <fcntl.
h>
#include <malloc.h>
#include <math.h>
#include <stdio.h>
#include <string.h>
#include <sys/mman.h>
#include <sys/types.h>
#include <values.h>
#include <sys/stat.h>
/*
usage:
align regions -D '.PARA' -d '.End of Sentence' file1 file2
outputs two files: filel.al ~ file2.al
hard regions are delimited by the -D arg
soft regions are delimited by the -d arg
*/
#define dist(x,y) distances[(x) * ((ny) + 1) + (y)]
#define pathx(x,y) path x[(x) * ((ny) + i) + (y)]
#define pathy(x,y) path_y[(x) * ((ny) + 1) + (y)]
#define MAX_FILENAME 286
#define BIG DISTANCE 2800
/* Dynamic Programming Optimization */
struct alignment {
int xl;
int yl;
int x2;
int y2;
int d;
};
char *hard_delimiter = NULL;
char *soft_delimiter = NULL;
int verbose = O;
/* utility functions */
I~ -D arg *I
I* -d arg *I
I* -v arg *I
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Computational Linguistics Volume 19, Number 1
char *readchars(), **readlines(), **substrings();
void err();
/*
seq_align by Mike Riley
x and y are sequences of objects, represented as non-zero ints,
to be aligned.
dist_funct(xl, yl, x2, y2) is a distance function of 4 args:
dist_funct(xl, yl, O, O) gives cost of substitution of xl by yl.
dist_funct(xl, O, O, 01 gives cost of deletion of xl.
dist_funct(O, yl, O, 01 gives cost of insertion of yl.
dist_funct(xl, yl, x2, 01 gives cost of contraction of (xl,x2) to yl.
dist_funct(xl, yl, O, y2) gives cost of expansion of xi to (yl,y2).
dist_funct(xl, yl, x2, y2) gives cost to match (xl,x2) to (yl,y2).
align is the alignment, with (align[i].xl, align[i].x2) aligned
with (align[i].yl, align[i].y2). Zero in align[].xl and align[].yl
correspond to insertion and deletion, respectively. Non-zero in
align[].x2 and align[].y2 correspond to contraction and expansion,
respectively, align[].d gives the distance for that pairing.
The function returns the length of the alignment.
*/
int
seq_align(x, y, nx, ny, dist_funct, align)
int *x, *y, nx, ny;
int (*dist_funct)();
struct alignment **align;
int *distances, *path_x, *path_y, n;
int i, j, oi, oj, di, dj, dl, d2, d3, d4, d5, d6, dmin;
struct alignment *ralign;
distances = (int *) malloc((nx + I) * (ny + i) * sizeof(int));
path_x = (int *) malloc((nx + i) * (ny + I) * sizeof(int));
path_y = (int *) malloc((nx + I) * (ny + i) * sizeof(int));
ralign = (struct alignment *) malloc((nx + ny)
• sizeof(struct alignment));
for(j = O; j <= ny; j++) {
for(i = O; i <= nx; i++) {
dl = i>O &~ j>O ? /* substitution */
dist(i-l, j-l) + (*dist_funct)(x[i-l], y[j-1], O, O)
: MAXINT;
d2 = i>O ? /* deletion */
dist(i-l, j) + (*dist_funct)(x[i-l], O, O, O)
: MAXINT;
d3 = j>O ? /* insertion */
dist(i, j-l) + (*dist_funct)(O, y[j-1], O, O)
92
William A. Gale and Kenneth W. Church Program for Aligning Sentences
: MAXINT;
d4 = i>l && j>O ? /* contraction */
dist(i-2, j-l) + (~dist_funct)(x[i-2], y[j-l], x[i-l], O)
: MAXINT;
d5 = i>O && j>l ? /~ expansion */
dist(i-l, j-2) + (~dist_funct)(x[i-l], y[j-2], O, y[j-l])
: MAXINT;
d6 = i>l && j>l ? /~ melding ~/
dist(i-2, j-2) + (*dist_funct)(x[i-2], y[j-2], x[i-l], y[j-l])
: MAXINT;
dmin = di;
if(d2<dmin) dmin=d2;
if(d3<dmin) dmin=d3;
if(d4<dmin) dmin=d4;
if(d5<dmin) dmin=dS;
if(d6<dmin) dmin=d6;
if(dmin == MAXINT) {
dist(i,j) = O;
}
else if(dmin == dl) {
dist(i,j) = dl;
pathx(i,j) = i-l;
pathy(i,j) = j-l;
}
else if(dmin == d2) {
dist(i,j) = d2;
pathx(i,j) = i-l;
pathy(i,j) = j;
}
else if(dmin == d3) {
dist(i,j) = d3;
pathx(i,j) = i;
pathy(i,j) = j-l;
}
else if(dmin == d4) {
dist(i,j) = d4;
pathx(i,j) = i-2;
pathy(i,j) = j-l;
}
else if(drain == d5){
dist(i,j) = d5;
pathx(i,j) = i-l;
pathy(i,j) = j-2;
}
else /* dmin == d6 */ {
dist(i,j) =
d6;
pathx(i,j) = i-2;
pathy(i,j) = j-2;
}
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Computational Linguistics Volume 19, Number 1
}
n=O;
for(i=nx, 3=ny ; i>O II j>O ; i = oi, j = oj) {
oi = pathx(i, j) ;
oj = pathy(i, j) ;
di = i - oi;
dj = j - o j;
if(di == i a~ dj == I) { /* substitution */
ralign[n] .xl = x[i-i] ;
ralign[n].yl = y[j-i];
ralign [n] .x2 = O;
ralign In] .y2 = 0 ;
ralign[n++].d = dist(i, j) - dist(i-i, j-i);
else if(di == 1 ~a dj == O) { /* deletion */
ralign[n] .xl = x[i-l] ;
ralign[n] .yl = O;
ralign[n] .x2 = O;
ralign [n] . y2 = 0 ;
ralign[n++].d = dist(i, j) - dist(i-l, j);
else if(di == 0 ~ dj == I) { /* insertion */
ralign[n] .xl = O;
ralign[n].yl = y[j-l];
ralig~ InS .x2 = O;
ralign [n] . y2 = 0 ;
ralign[n++] .d = dist(i, j) - dist(i, j-l);
else if(dj == i) { /* contraction */
ralign[n] .xl = x[i-2] ;
ralign[n].yl = y[j-l];
ralig~[n].x2 = x[i-l] ;
ralign [n] . y2 = 0 ;
ralign[n++].d = dist(i, j) - dist(i-2, j-l);
else if(di == i) { /* expansion */
ralign[n].xl = x[i-l];
ralign In] . yl = y [j -2] ;
ralign[n] .x2 = O;
ralign[n].y2 = y[j-l];
ralign[n++].d = dist(i, j) - dist(i-l, j-2);
}
else /* di == 2 aa dj == 2 */ { /* melding */
ralign[n].xl = x[i-2] ;
ralign In] . yl = y [j -2] ;
ralign[n].x2 = x[i-l] ;
ralign[n].y2 = y[j-l];
94
William A. Gale and Kenneth W. Church Program for Aligning Sentences
ralign[n++].d = dist(i, j) - dist(i-2, j-2);
}
}
*align = (struct alignment *) malloc(n * sizeof(struct alignment));
for(i=O; i<n; i++)
bcopy(ralign + i, (*align) + (n-i-l), sizeof(struct alignment));
free(distances);
free(path_x);
free(path_y);
free(ralign);
return(n);
}
/* Local Distance Function */
/* Returns the area under a normal distribution
from -inf to z standard deviations */
double
pnorm(z)
double z;
{
double t, pd;
t
=
1/(1
+
0.2316419 *
z);
• pd = 1 -
0,3989423 *
exp(-z * z/2) *
((((1.330274429 * t - 1.821255978) * t
+ 1.781477937) * t - 0.356563782) * t + 0.319381530) * t;
/* see Abramowitz, M., and I. Stegun (1964), 26.2.17 p. 932 */
return(pd);
}
/* Return -100 * log probability that an English sentence of length
lenl is a translation of a foreign sentence of length len2. The
probability is based on two parameters, the mean and variance of
number of foreign characters per English character.
*/
int
match(lenl, len2)
int lenl, len2;
{
double z, pd, mean;
double c = 1;
double s2 = 6.8 ;
if(lenl==O && len2==O) return(O);
mean = (lenl + len2/c)/2;
z = (c * lenl - len2)/sqrt(s2 * mean);
/* Need to deal with both sides of the normal distribution */
if(z < O) z = -z;
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Computational Linguistics Volume 19, Number 1
pd = 2 * (1 - pnorm(z));
if(pd > O) return((int)(-lO0 * log(pd)));
else return(BIG_DISTANCE);
}
int
two_side_distance(xl, yl, x2, y2)
int xl, yl, x2, y2;
int penalty21 = 230;
log([prob of 2-1 match]
int penalty22 = 440;
Iog([prob of 2-2 match]
int penaltyOl = 450;
log([prob of 0-i match] / [prob of I-i match])
/* -i00
*
/ [prob of 1-1 match]) */
/* -100 *
/ [prob of 1-1 match]) */
/* -100 * */
if(x2 == 0 ~& y2 == O)
if(x1 == O) /* insertion */
return(match(x1, yl) + penalty01);
else if(y1 == O) /* deletion */
return(match(x1, yl) + penalty01);
else return (match(xl, yl)); /* substitution */
else if(x2 == O) /* expansion */
return (match(x1, yl + y2) + penalty21);
else if(y2 == O) /* contraction */
return(match(xl + x2, yl) + penalty21);
else /* merger */
return(match(x1 + x2, yl + y2) + penalty22);
}
/* Functions for Manipulating Regions */
struct region {
char **lines;
int length;
};
void
print_region(fd, region, score)
int score;
FILE *fd;
struct region *region;
{
char **lines, **end;
lines = region->lines;
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William A. Gale and Kenneth W. Church Program for Aligning Sentences
end = lines + region->length;
for( ; lines < end ; lines++)
fprintf(fd, "Zs\n", *lines);
}
int
length_of_a_region(region)
struct region *region;
{
int result;
char **lines, **end;
lines = region->lines;
end = lines + region->length;
result = end - lines;
for( ; lines < end; lines++)
result += strlen(*lines);
return(result);
}
int *
region_lengths(regions, n)
struct region *regions;
int n;
{
int i;
int *result;
result = (int *)malloc(n * sizeof(int));
if(result == NULL) err("malloc failed");
for(i = O; i < n; i++)
result[i] = length of_a_region(regions[i]);
return(result);
}
struct region *
find_sub_regions(region, delimiter, len_ptr)
struct region *region;
char *delimiter;
int *len_ptr;
struct region *result;
char **i, **lines, **end;
int n = O;
lines = region->lines;
end = lines + region->length;
for(l
= lines; i < end; I++)
if(delimiter && strcmp(*l, delimiter) == O) n++;
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Computational Linguistics Volume 19, Number 1
result = (struct region ~)calloc(n+l, sizeof(struct region));
if(result == NULL) err("malloc failed");
*len_ptr = n;
n = O;
result[O].lines = lines;
for(1 = lines; 1 < end; l++)
if(delimiter &~ strcmp(*l, delimiter) == O) {
result[n].length = 1 - result[n].lines;
result[n+l].lines = I+i;
n++;
}
result[n].length = 1 - result[n].lines;
if(n != *len_ptr) {
fprintf(stderr, "find_sub_regions: n = ~d, *len_ptr = ~d\n", n,
*len_ptr);
exit(2);
}
return(result);
}
/* Top Level Main Function */
int
main(argc, argv)
int argc;
char **argv;
char **linesl, ~*lines2;
int number_oflinesl, number_of_lines2;
struct region ~hard_regionsl, ~hard_regions2, ~soft regionsl,
~soft_regions2;
struct region ~hard_endl, ~hard_end2, tmp;
int number_ofhard_regionsl;
int number_of hard_regions2;
int number_ofsoft_regionsl;
int number_of soft_regions2;
int ~lenl, ~len2;
int c, n, i, ix, iy, prevx, prevy;
struct alignment ~align, ~a;
FILE *outl, ~out2;
char filename[MAXFILENAME];
extern char ~optarg;
extern int optind;
/* parse arguments */
while((c = getopt(argc, argv, "vd:D:"))
switch(c) {
case 'v''
verbose = 1;
break;
case 'd':
soft delimiter = strdup(optarg);
!= EOF)
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William A. Gale and Kenneth W. Church Program for Aligning Sentences
break;
case 'D':
hard_delimiter = strdup(optarg);
break;
default:
fprintf(stderr, "usage: align_regions [d (soft delimiter)]
(hard delimiter)]\n");
exit(2);
}
if(argc != optind + 2) err("wrong number of arguments");
/* open output files */
sprintf(filename, "~s.al", argv[optind]);
out1 = fopen(filename, "w");
if(outl == NULL) {
fprintf(stderr, "can't open ~s\n", filename);
exit(2);
}
sprintf(filename, "~s.al", argv[optind+l]);
out2 = fopen(filename, "w");
if(out2 == NULL) {
fprintf(stderr, "can't open ~s\n", filename);
exit(2);
}
[D
linesl = readlines(argv[optind], &number_of_linesl);
lines2 = readlines(argv[optind+l], &number_of_lines2);
tmp.lines = linesl;
tmp.length = number_of_linesl;
hard_regionsl = find_subregions(&tmp, hard_delimiter,
~number_of_hard_regionsl);
tmp.lines = lines2;
tmp.length = number_of_lines2;
hard_regions2 = find_sub_regions(&tmp, hard_delimiter,
&number_of_hard_regions2);
if(number_ofhard_regionsl != number_of_hard_regions2)
err("align_regions: input files do not contain the
same number of hard regions");
hard_endl = hard_regionsl + number_of_hard_regionsl;
hard_end2 = hard regions2 + number of_hard regions2;
for( ; hard_regionsl < hard_endl ; hard regionsl++, hard_regions2++) {
soft_regionsl = find_sub_regions(hard_regionsl[O], soft_delimiter,
&number_of_soft_regionsl);
soft_regions2 = find_sub_regions(hard_regions2[O], soft_delimiter,
&number_of_soft_regions2);
lenl = region_lengths(soft_regionsl, number_of_soft_regionsl);
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Computational Linguistics Volume 19, Number 1
len2 = region_lengths(soft_regions2, number_of_soft_regions2);
n = seq_align(lenl, len2, number_of_soft_regionsl,
number_of_soft_regions2,
two_side_distance, ~align);
prevx = prevy = ix = iy = O;
for(i = O; i < n; i++) {
a = aalign[i];
if(a->x2 > O) ix++; else if(a->xl == O) ix--;
if(a->y2 > O) iy++; else if(a->yl == O) iy--;
if(a->xl == 0 a~ a->yl == 0 ~& a->x2 == 0 ~ a->y2 == O)
{ix++; iy++;}
ix++;
iy++;
if(verbose) {
fprintf(outl, ".Score ~dkn", a->d);
fprintf(out2, ".Score ~dkn", a->d);
}
for( ; prevx < ix; prevx++)
print_region(outl, soft_regionsl[prevx], a->d);
fprintf(outl, "~sin", soft_delimiter);
for( ; prevy < iy; prevy++)
print_region(out2, soft_regions2[prevy], a->d);
fprintf(out2, "Zskn", soft_delimiter);
}
fprintf(outl, "~skn",
hard_delimiter);
fprintf(out2, "~skn", hard_delimiter);
free(align);
free(soft_regionsl);
free(soft_regions2);
free(lenl);
free (len2) ;
}
}
/~ Utility Functions ~/
void
err(msg)
char ~msg;
{
fprintf(stderr, "~ERROR~: %s\n", msg);
exit(2);
}
/~ return the contents of the file as a string
and stuff the length of this string into len_ptr ~/
char
readchars(filen~ne, len_ptr)
char ~filename;
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William A. Gale and Kenneth W. Church Program for Aligning Sentences
int ~len ptr;
FILE *fd;
char *result;
struct stat stat_buf;
fd = fopen(filename, "r");
if(fd == NULL) err("open failed");
if(fstat(fileno(fd), &stat_buf) == -I)
err("stat failed");
*len_ptr = stat buf.st_size;
result = malloc(*len_ptr);
if(result == NULL) err("malloc failed\n");
if(fread(result, sizeof(char), *len_ptr, fd) != ~len_ptr)
err("fread failed");
if(fclose(fd) == -i)
err("fclose failed");
return(result);
}
/* split string into a number of substrings delimited by a delimiter
character
return an array of substrings
stuff the length of this array into len_ptr */
char **
substrings(string, end, delimiter, len_ptr)
char *string, *end, delimiter;
int *len_ptr;
char *s, **result;
int i = O;
while(string < end && *string == delimiter) string++;
for(s = string; s < end; s++)
if(*s == delimiter) i++;
*len_ptr = i;
result = (char **)malloc(sizeof(char *) * (i+l));
if(result == NULL) err("malloc failed");
i = O;
result[i++] = string;
for(s = string; s < end; s++)
if(~s == delimiter) {
result[i++] = s+l;
*s = O;
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}
i--; /*the last entry is beyond the end*/
if(i != *len_ptr) {
fprintf(stderr, "align_regions: confusion; i = ~d; *len_ptr = ~d\n", i,
*len_ptr);
exit(2);
}
return(result);
}
/* return an array of strings, one string for each line of the file
set len_ptr to the number of lines in the file */
char **
readlines(filename, len_ptr)
char *filename;
int *len_ptr;
char *chars;
int number_of_chars;
chars = readchars(filename, ~number_of_chars);
return(substrings(chars, chars + number_of_chars, '\n' , len_ptr)) ;
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