This paper describes a pilot study in which we collected a small learner corpus of Arabic, developed a tagset for error-annotation of Arabic learner data, tagged the data for error 1, and performed simple Computer-aided Error Analysis (CEA)

Abuhakema, Ghazi

Feldman, Anna

Fitzpatrick, Eileen

English

Montclair State University Digital Commons

Montclair State University Montclair State University Digital Commons Department of Linguistics Faculty Scholarship and Creative Works Department of Linguistics 11-17-2008 ARIDA: An Arabic Interlanguage Database and Its Applications: A Pilot Study Anna Feldman Montclair State University, feldmana@montclair.edu Ghazi Abuhakema College of Charleston Eileen Fitzpatrick Montclair State University, fitzpatricke@montclair.edu Follow this and additional works at: https://digitalcommons.montclair.edu/linguistics-facpubs  Part of the Linguistics Commons MSU Digital Commons Citation Feldman, Anna; Abuhakema, Ghazi; and Fitzpatrick, Eileen, "ARIDA: An Arabic Interlanguage Database and Its Applications: A Pilot Study" (2008). Department of Linguistics Faculty Scholarship and Creative Works. 18. https://digitalcommons.montclair.edu/linguistics-facpubs/18 This Conference Proceeding is brought to you for free and open access by the Department of Linguistics at Montclair State University Digital Commons. It has been accepted for inclusion in Department of Linguistics Faculty Scholarship and Creative Works by an authorized administrator of Montclair State University Digital Commons. For more information, please contact digitalcommons@montclair.edu. ARIDA: An Arabic Interlanguage Database and Its Applications: A Pilot StudyAnna Feldman and Ghazi Abuhakema and Eileen FitzpatrickMontclair State UniversityMontclair, New Jersey, 07043, USA{feldmana,abuhakemag,fitzpatricke}@mail.montclair.eduAbstractThis paper describes a pilot study in which we collected asmall learner corpus of Arabic, developed a tagset for error-annotation of Arabic learner data, tagged the data for er-ror1, and performed simple Computer-aided Error Analysis(CEA).Language Learner Corpora and ApplicationsLearner corpora research uses the methods and tools of Sec-ond Language Acquisition (SLA) studies and corpus linguis-tics to gain better insights into authentic learner language atdifferent levels – lexis, grammar, and discourse.One application of learner corpora is Contrastive Interlan-guage Analysis (CIA), which involves two types of compar-ison – 1) native speech (NS) vs. non-native speech (NNS)to highlight the features of nativeness and non-nativeness oflearner language; 2) two or more varieties of NNS to deter-mine whether non-native features are limited to one groupof non-native speakers (in which case it is most probablya transfer-related phenomenon), or whether they are sharedby several groups of learners with different mother tonguebackgrounds (which would point to a developmental issue).Another application is Computer-aided Error Analysis(CEA) for identifying the sources of error (L1 interference,features of novice writing in the new culture, limited vo-cabulary and language structure, etc.). For this application,error annotation is essential.Error TaggingThere are two ways to annotate learner data for error. Oneapproach is to reconstruct the correct form (?). The other ap-proach is to mark different types of errors with special tags(?). The former is used for developing instructional materi-als that can provide (automatic) feedback to learners; the lat-ter is used for SLA research to compare type of error and er-ror frequency among different learners at different levels oflanguage development. We have begun our study of learnerArabic using both reconstruction and special tag annotation,with the FRIDA (?) tagset for French as our model for thelatter approach.Copyright c© 2008, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.1We thank Reem Faraj for helping us with data annotation.Applications of Error TaggingError tagging is a highly time-and labor-consuming task.At the same time, a corpus annotated for error provides aninvaluable resource for SLA research and instruction. ForSLA researchers, errors can reveal much about the processby which the second language (L2) is acquired and the kindsof strategies or methodology the learners use in that process.For language instructors, errors can give hints about the ex-tent to which learners have acquired the language systemand what they still need to learn. Finally, for learners them-selves, access to the data marked for error provides impor-tant feedback for improvement.A Pilot Arabic Learner CorpusTo the best of our knowledge, there are no learner Arabiccorpora available for public use. In general, there is little re-search done in the area of data-driven instructional materialsdevelopment. Prior lack of interest in Arabic as a foreignlanguage, the existence of more than thirty dialects and sub-dialects of the language, and previous technical difficultiesin dealing with non-roman scripts have meant that resourcesfor the systematic investigation of the acquisition of Arabicby non-native speakers are extremely scarce.Error Annotation of ArabicLinguistic Properties of Arabic and Error TaggingThe most salient difference between French and Arabic isin the basic word formation process, French being a stemand affix language and Arabic being a trilateral root lan-guage. However, like French, Arabic has inflectional affixesthat 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.The Learner DataWe have analyzed eight different texts written by learnersof Arabic as a Foreign Language. The level of the textsranged between intermediate (3,818 tokens) and advanced(4,741 tokens). The students are Americans whose nativelanguage is English. They studied Arabic in an intensiveprogram and then went to study abroad in Arab countries.Some of the texts were written during their study years in225Proceedings of the Twenty-First International FLAIRS Conference (2008)the United States and others represented their productionswhile studying abroad. The classification into the interme-diate and advanced levels was done based on the guidelinesprovided by the American Council on the Teaching of For-eign Languages (ACFTL) to rate written texts.The FRIDA Tagset Applied to ArabicWe have adopted FRIDA’s highest level of tagging, the do-main, with only one addition: diglossia, a common errorwhen students are exposed to the many dialects of Arabic.For the intermediate level, the error categories, we deletedsome tags and added others. The tags that we dropped in-clude upper/lower case, and auxiliary (Arabic does not havethem), diacritics, and homonymy, which will only occur infully voweled texts and do not appear in learner writing. Wedo not anticipate using these tags on a larger scale set.In terms of phonology, we added the long/short voweldistinction, emphatic/non-emphatic consonants, nunation (amark of indefiniteness), hamza (a glottal stop that learnersoften do not hear), and shadda (consonant doubling).In terms of morphology, the phenomenon of partial orweak agreement in Arabic caused us to modify the tagsetto include full inflection, partial inflection, zero inflection,which FRIDA does not need for French, as well as infix-ation, gender agreement, (in)definite agreement, numberagreement (Arabic utilizes different types of agreement),and negation (there exist a few negation particles based onthe form of the sentence and verb tense). In terms of syn-tax, we added definite and indefinite structure (different from(in)definite agreement), verb pattern confusion, and wordconfusion.In terms of style, we kept heavy, though we found no in-stances of turgid writing in our samples. We added pallid,for writing that is oversimplified.The reader is invited to visit http://chss.montclair.edu/˜feldmana/publications/flairs21-data/ to see the details of the tagset. Weanticipate that we will need to add more tags as we dealwith texts of beginning and highly advanced learners.Additionally as we apply FRIDA’s third tagging level,covering word categories, we anticipate that we will need toadjust it to fulfill particular needs the corpus will dictate interms of adding, expanding or deleting tags.Computer-Aided Error Analysis (CEA)The data discussed here is available at http://chss.montclair.edu/˜feldmana/publications/flairs21-data/.Frequency of error typesThe frequency of error types based on student level al-ready provides useful data for pedagogical purposes. Weclassified the most frequent errors by learner level. Onenotable difference between the intermediate and advancedwriters is that the former are still partly struggling withphonological/orthographic issues (the glottal stops known as‘hamza’, for which these students have difficulty masteringthe spelling rules or even hearing) while the latter group haveleft these errors behind and are struggling, not surprisingly,with features of advanced writing such as word order andcohesion. Both groups still have difficulties with lexis andthe morphologically marked agreement.Part of Speech (POS) usageOur initial evaluation of POS usage suggests that the ad-vanced students’ active vocabulary is not necessarily muchricher: not only are the POS usages similar, but also thenumber of different members belonging to the same cate-gory (i.e. types) is comparable. Advanced students do notseem to use a greater variety of verbs, nouns, or adjectives.We hypothesize that this is one of their error-avoidancestrategies.Patterns of Underuse and OveruseWe used the ”missing” and ”redundant” error categories tosearch for patterns of overuse and underuse. We found thatthe patterns of underuse and overuse are largely an L1 trans-fer phenomenon. The differences between the beginners andadvanced students’ writing are in the type of errors. Theformer make more grammatical mistakes, whereas the latterhave more stylistic and lexical issues.Ongoing workOur intention is to test this tagset on our most elementarywriting students’ work and modify it further if necessary.We plan to add an additional layer of annotation – recon-struction – where the mistakes will be corrected. This willallow us to run the standard tools, such as a POS-tagger anda parser to be able to analyze data further and start the workon automatic Arabic tutors. The POS-tagged data is impor-tant for implementing a more reliable error analysis as wellas for further parsing. The parse trees will give us data aboutthe syntactic development of Arabic learners, an area thathas not been investigated enough, and will shed light on theredundant/missing errors mentioned in the paper.In the future, we also plan to compare native (NS) andnon-native writings (NNS) to highlight the features of na-tiveness and non-nativeness of learner language.With respect to overuse/underuse patterns, since we no-ticed so many L1 transfer-related phenomena, we plan tocompare this present data with Hebrew speakers learningArabic. Modern Hebrew is another Semitic language withproperties similar to Arabic and we expect that the patternsof overuse and underuse will be different for these students.226

ARIDA: An Arabic Interlanguage Database and Its Applications: A Pilot Study

This paper describes a pilot study in which we collected a small learner corpus of Arabic, developed a tagset for error annotation and performed simple Computer-aided Error Analysis (CEA) on the data. For this study, we adapted the French Interlanguage Database (FRIDA) (Granger, 2003a) tagset to the data. We chose FRIDA in order to keep our tagging in line with a known standard. The paper describes the need for learner corpora, the learner data we have collected, the tagset we have developed, its advantages and disadvantages, the preliminary CEA results, other potential applications of the error-annotated corpus of Arabic, and the error frequency distribution of both proficiency levels as well as our ongoing work

ARIDA: An Arabic Interlanguage Database and Its Applications: A Pilot Study

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