Annotating an Arabic Learner Corpus for Error
Ghazi Abuhakema, Reem Faraj, Anna Feldman, Eileen Fitzpatrick
Montclair State University
Montclair NJ 07043
Email: email@example.com, firstname.lastname@example.org, email@example.com,
This paper describes an ongoing project in which we are collecting a learner corpus of Arabic, developing a tagset for error annotation and
performing Computer-aided Error Analysis (CEA) on the data. We adapted the French Interlanguage Database FRIDA tagset (Granger,
2003a) to the data. We chose FRIDA in order to follow a known standard and to see whether the changes needed to move from a French to
an Arabic tagset would give us a measure of the distance between the two languages with respect to learner difficulty. The current
collection of texts, which is constantly growing, contains intermediate and advanced-level student writings. We describe the need for such
corpora, the learner data we have collected and the tagset we have developed. We also describe the error frequency distribution of both
proficiency levels and the ongoing work.
We describe a pilot study in which we developed a tagset
for error-annotation of Arabic learner data. We compiled
a small pilot corpus of Arabic learner written productions
and adapted the French Interlanguage Database FRIDA
tagset (Granger, 2003a) to the data. We chose FRIDA in
order to follow a known standard and to see whether the
changes needed to move from a French to an Arabic
tagset would give us a measure of the distance between
the two languages with respect to learner difficulty.
2. Language Learner Corpora
Computer Learner Corpus research is grounded in both
corpus linguistics and Second Language Acquisition
(SLA) studies. It uses the methods and tools of corpus lin-
guistics to gain better insight into authentic learner lan-
guage at different levels – lexis, grammar, and discourse.
(Pravec, 2002; Granger, 2003b).
2.1 Contrastive Interlanguage Analysis (CIA)
Learner corpus research has concentrated on Contrastive
Interlanguage Analysis (CIA), which involves two types
of comparison – 1) native productions (NS) vs. non-native
productions (NNS) to highlight the features of non-
nativeness in the learner language; 2) two or more
varieties of NNS to determine whether non-native
features are limited to one group of learners, in which
case they are most probably transfer-related phenomena,
or whether they are shared by several groups of learners,
which would point to a developmental, or interlanguage,
2.2 Computer-Aided Error Analysis
Computer-aided Error Analysis (CEA) has led to a much
more limited number of publications than CIA due to the
cost of manual error annotation. Apart from articles
describing error tagging systems, there are a few articles
covering certain specific error categories including lexical
errors (Man-Lai et al., 1994; Källkvist, 1995; Lenko-
Szymanska, 2003), tense errors (Granger, 1999;
Fitzpatrick and Seegmiller, 2004) and a more recent
article (Neff et al., 2007) covering the range of error types
in the ICLE corpus from Spain. These analyses offer great
promise for identifying the sources of error (L1
interference, features of
vocabulary and language structure, etc.) so the need to
annotate for error and to reduce the cost of annotation by
automating where possible is great.
novice writing, limited
3. Error Tagging
There are two ways to annotate learner data for error. One
approach is to reconstruct the correct form (e.g. Fitz-
patrick and Seegmiller, 2001). The other approach is to
mark different types of errors with special tags (e.g.
Granger, 2003a). The former is used for developing in-
structional materials that can provide (automatic)
feedback to learners; the latter is used for SLA research to
compare type of error and error frequency among
different learners at different levels of language
3.1 Applications of Error Tagging
Error tagging is a highly time- and labor-consuming task.
At the same time, a corpus annotated for error provides an
invaluable resource for SLA research and practice. For
researchers, errors can reveal much about the process by
which L2 is acquired and the kinds of strategies the
learners use in that process. For language instructors,
errors can give hints about the extent to which learners
have acquired the language system and what they still
need to learn. Finally, for learners themselves, access to
the data marked for error provides important feedback for
3.2 FRIDA (French Interlanguage Database)
Error tagging in FRIDA implements both reconstruction
and tagsets. To develop an error tagset for learner Arabic,
we adapted the FRIDA tagset designed specifically for
French. We chose FRIDA because of the explicit
description of the tags in Granger (2003a). FRIDA is a
three-level error annotation system, with 9 domains, 36
error categories and 54 word categories. The domain level
is the most general: it specifies whether the error concerns
typography and spelling, morphology, grammar, lexis,
syntax, punctuation, register, or style. Each error domain
is subdivided into a variable number of error categories.
For example, the lexical domain L groups all lexical
errors due to: 1) insufficient knowledge of the conceptual
meaning of words; 2) violations of the co-occurrence
patterns of words; 3) violations of the grammatical
complementation patterns of words. The word categories
(adjective, adverb, article, etc.) are subdivided into 54
subcategories, such as ‘simple, comparative, superlative,
complex for adjective errors. This particular tier makes it
possible to sort errors by grammatical category and to
draw up a list of relevant errors for each category.
4. A Pilot Arabic Learner Corpus
To the best of our knowledge, there are no learner Arabic
corpora available for public use. Prior lack of interest in
Arabic as a foreign language, the existence of more than
thirty dialects and subdialects of the language, and
previous technical difficulties with non-roman scripts
have meant that resources for the systematic investigation
of the acquisition of Arabic by non-native speakers are
extremely scarce. Currently, not only is there a lack of
learner corpora resources for critical languages, but there
is no portable software that can be easily adapted to
generate instructional materials automatically based on
specified criteria, such as the level of linguistic
complexity, different levels of competence, genre, target
linguistic structure or discourse style. The current demand
for the rapid generation of teaching materials for Arabic
makes the creation and internet dissemination of a learner
corpus such as this a critical need.
5. Error Annotation of Arabic
5.1 Linguistic Properties of Arabic Relevant to
The most salient difference between French and Arabic is
in the basic word formation process, French being a stem
and affix language and Arabic being a triliteral root
language. However, like French, Arabic has inflectional
affixes that mark gender, person, number, tense, etc. In
addition, there are general errors that will be present for
all L2s, e.g., errors involving word order, missing or
confused elements, and spelling.
5.2 The Learner Data
We have analyzed eight different texts written by learners
of Arabic as a Foreign Language. The level of the
students was either intermediate (3818 words) or
advanced (4741 words). The students are American native
speakers of English who studied Arabic in an intensive
program and then went to study abroad in Arab countries.
Some of the texts were written during their study in the
United States and others represent their writing while
For this pilot study, the tagset was developed by one
author and applied by this author and a second author on
different data in order to test the coverage of the tags.
Once the tagset is complete, we will test for interrater
5.3 The FRIDA Tagset Applied to Arabic
We have adopted FRIDA’s first level of tagging with only
one addition: diglossia, a common error when students
are exposed to the many dialects of Arabic. For the
second level, we deleted some tags and added others. The
tags that we dropped include upper/lower case, auxiliary
and euphony (Arabic does not have these), diacritics, and
homonymy, which will only occur in fully voweled texts
and do not appear in learner writing. We do not anticipate
using these tags on a larger scale set.
In terms of phonology, we added the long/short vowel
distinction, emphatic/non-emphatic consonants, nunation
(a mark of indefiniteness), hamza (a glottal stop that
learners often do not hear), and shadda (consonant
doubling). In terms of morphology and syntax, we added
infixation, verb pattern confusion, negation (Arabic has
several negation particles based on the form of the
sentence and verb tense), and definite and indefinite
structure (different from (in)definite agreement). The
phenomenon of partial, or weak, agreement in Arabic
caused us to modify the tagset to include full inflection,
partial inflection, and zero inflection, which FRIDA does
not need for French. We also made minor modifications
to gender agreement, (in)definite agreement, and number
agreement. In terms of style, we kept ‘heavy’, though we
found no instances of turgid writing in our samples. We
added ‘pallid’, for writing that is oversimplified.
We also anticipate that we will need more tags as we deal
with texts of beginning and highly advanced learners.
Additionally, as we apply FRIDA’s third tagging level,
we anticipate that we will need to adjust it to fulfill
particular needs the corpus will dictate.
5.4 The Tagset for Learner Arabic
Table 1 shows the Arabic tagset we are currently using.
The first column shows the error domains while the
second demonstrates the error categories. For the tags
themselves, we either used the initial(s) and/or the root or
part of the root of the word that represents each domain
and category. The tags use the Arabic script and appear in
brackets in the table.
Vowel length confusion
??????ا ????ا فو?? ??? ????ا
??????او ??????ا فو???ا >
Consonant doubling (shaddat)
Other spelling errors
Inflection – full
ى??أ ?????ه ء???أ >
ق?????ا- ??د???ا >
ق?????ا– ?????ا >
???????ا ق?????ا >
Inflection – partial
ف???ا ?? ع?????ا( ف?????ا ???
Inflection – zero
??????ا ?? ????ا >
??????ا ?? ???????ا >
????ا ?? ???????ا >
د???ا ?? ???????ا >
ا ل?????او م?????? ????? >
????ا ????? >
???ا ????? >
????ا ????? >
ت?????ا ??? >
ةد???? ???آ >
ة??از ???آ >
??????ا ما????ا >
??????ا ت???? >
??????ا ?? ????ا >
ةد???? ????? ???? >
ة??از ????? ????
?????? ء???أ >
Table 1. The Error Tagset for Arabic
While our corpus was not large enough to test interrater
reliability, our test of the tagset usability yielded results
that will affect our work as we tag a larger corpus.
Each annotator covered only 500 words of text per hour
due to the need to go up and down the levels of annotation
to mark each error. A pull-down menu of tags at each
level is planned to speed the annotation.
The frequency of error types based on student level
already provides useful data for pedagogical purposes.
Table 2 shows the most frequent errors by learner level.
Intermed., wc= 3818
Advanced, wc= 4741
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Table 2. Most frequent errors by learner level.
5.6 Error Frequency Distributions
The tag frequency distributions are not surprising, but will
be useful in terms of pedagogy. One notable difference
between the intermediate and advanced writers is that the
former are still
phonological/orthographical issues (e.g., the glottal stops
known as ‘hamza’, which are difficult to hear or involved
in spelling rules) while the latter group have left these
errors behind and are struggling, not surprisingly, with
features of advanced writing like word order and cohesion.
Both groups still have difficulties with lexis and the
morphologically marked agreement.
6. Ongoing Work
Our intention is to test this tagset on our most elementary
writing students’ work and modify further if necessary.
We will continue error tagging on the three levels of
beginning, intermediate, and advanced, and make the
tagged essays publicly available via the web for further
second language acquisition analysis and design of
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