Khaled Shaalan Doaa Samy Marwa Magdy Towards Resolving Morphological Ambiguity in Arabic Intelligent Language Tutoring Framework Khaled Shaalan Doaa Samy Marwa Magdy 1 1

Outlines Introduction. Arabic Morphological Ambiguity Problem The Proposed Disambiguation System Evaluation & Results Conclusions 2

Outlines Introduction Arabic Morphological Ambiguity Problem The Proposed Disambiguation System Evaluation & Results Conclusions 3

Introduction curriculum sequence Adaptive navigation Intelligent Language Tutoring System adaptive presentation A computer-based educational system that allows simulation of a human tutor. its objective is to enhance teaching and learning of foreign languages. error remediation Intelligent feedback to student solutions 4

Main Challenges Lack of resources, such as Learner corpus for Arabic language Lack of tools dealing with ill-formed input In ILTS, relaxing the constraints of the language to analyze learner’s answer results in handling more interpretations than systems designed for only well-formed input 5

Main Challenges (cont‘d) Use techniques, such as constraints relaxation Analyzes قالت (said) In well formed systems: - 3rd female sg past verb In ILTS: 3rd person female sg 1st person sg past verb 2nd person male sg past verb 2nd person female sg past verb Intelligent Language Tutoring System Erroneous Learner Answer More Interpretation 6

Outlines Introduction Arabic Morphological Ambiguity Problem The Proposed Disambiguation System Evaluation & Results Conclusions 7

Arabic Morphological Ambiguity Arabic language is one of the Semitic languages that is defined as a diacritized language. Unfortunately, diacritics are rarely used in current Arabic writing conventions. So two or more words in Arabic are homographic 8

Different Interpretations Homographic Example Different Interpretations Lemma Word يعِد (bring back) أعاد يعد يعُد (return) عاد يعِد (promise) وعد یَعُدّ (count) عد یُعِدّ (prepare) أعد 9

Factors of Arabic Ambiguity For example, the word أسد can be interpreted as أسد (lion) or أسٌدً (I-Block). For example, the deletion of the letter (و) in taking the present (imperfect) tense of the trilateral root و-ع-د /w-E-d/, it appears in written texts as يعد (promise). For example, the perfect verb suffix ت can indicate either: 1) first person singular, 2) second person singular masculine, 3) second person singular feminine, or 4) third person singular feminine. For example, the فعل /faEala/ and فعًل /faE~ala/. Main Factors Orthographic alteration operations such as deletion Some verb prefixes and suffixes can be homographic Ambiguity of Undiacritized verb Arabic patterns Prefixes and suffixes can produce a form homographic with another word class 10

Outlines Introduction Arabic Morphological Ambiguity Problem The Proposed Disambiguation System Evaluation & Results Conclusions 11

Disambiguation Module Error Classification Module The Proposed System Affix Error Representation: Added final letter قال+ ت و Tense Voice active Subject Features 1st sg Object Features ‘’ Feature Value Stem قال Prefix Suffix ت Lexical Category verb Verb Type hollow Pattern فعل perfect Stem Error Representation: Added middle letter ق ل ا Suffix ‘’ Tense Voice active Mood indicative Subject Features 1st sg Object Features Feature Value Answer أقول Root ق-و-ل Lexical Category verb Pattern فعل Verb Type hollow Prefix أ imperfect Arabic ILTS Possible Word Analyses Verb tense error Verb conjugation Vowel letters Question: Build a sentence using the following roots: ق-و-ل، ح-ق، د-و-م /q-w-l, H-q, d-w-m/ The question goal is: conjugation hollow verb in imperfect tense active voice Word Analyzer Module Disambiguation Module قالتو الحق دائما (I always said the truth) Incorrect use of perfect verb instead of imperfect Item banking Selected Word Analysis Learner Answer Train the rules till get maximum likelihood for the corpus. Feedback Message Error Type Error Classification Module Tutoring Module 12

Prioritized Conditions Disambguation Module Prioritized Conditions Affix Collection Multiple Analyses Pattern Collection Train the rules till get maximum likelihood for the corpus. Selected Analysis No Action Multiple Analyses 13

Prioritized Conditions Yes Select Passive Analysis The question goal is to test passive voice No Select Active Analysis Item banking Train the rules till get maximum likelihood for the corpus. Multiple Analyses 14

Prioritized Conditions Yes Select Imperative Verb Analysis The question goal is to test imperative tense No Select perfect or imperfect verb Analysis Item banking Train the rules till get maximum likelihood for the corpus. Multiple Analyses 15

Example If the learner writes the following sentence: تباع جدتي الارز (My-grandmother sells the-rice ) The system produces two analyses: Third person singular feminine imperfect verb in the active voice with converted middle letter Third person singular feminine imperfect verb in the passive voice Train the rules till get maximum likelihood for the corpus. 16

Prioritized Conditions Disambguation Module Prioritized Conditions Affix Collection Multiple Analyses Pattern Collection Train the rules till get maximum likelihood for the corpus. Selected Analysis No Action Multiple Analyses 17

Example If the learner writes the following sentence: محمد تورطت في جريمة قتل (Mohamed was-involved in murder crime ) The system produces four analyses: First person singular perfect verb in the active voice Second person singular masculine perfect verb in the active voice Singular perfect verb conjugation in the active voice Second person singular feminine perfect verb in the active voice Third person singular feminine perfect verb in the active voice Train the rules till get maximum likelihood for the corpus. 18

Prioritized Conditions Disambguation Module Prioritized Conditions Affix Collection Multiple Analyses Pattern Collection Train the rules till get maximum likelihood for the corpus. Selected Analysis No Action Multiple Analyses 19

Example If the learner writes the following sentence: جدي وجدتي نقلوا الي بيت جديد (my-grandfather and my-grandmother moved to a new house ) The system produces two analyses: Third person masculine plural perfect verb in the active voice following the pattern 'فعل'. Third person masculine plural perfect verb in the active voice instead of dual Third person masculine plural perfect verb in the active voice following the pattern 'فعًل'. Train the rules till get maximum likelihood for the corpus. 20

Outlines Introduction. Arabic Morphological Ambiguity Problem. The Proposed Disambiguation System. Evaluation & Results Conclusions 21

Evaluations & Results Evaluation & Results A real test set that consists of 116 real Arabic sentences is collected The system successfully solved 60% of the cases 22

Evaluation Problems Classification For example, the erroneous word أجوب: the imperfect verb أجيب />u-jiyb/ (I-answer), 2) or imperfect verb أجوب />a-juwb/ (I-explore). Problems For example, the word تناول; 1) the noun تناول /tanAwul/ (dealing with/ eating), 2) the perfect verb تناول /tanAwala/ (he/it-dealt with/ ate), or 3) the imperfect verb تناول /tu-nAwil/ (hand over/ deliver). Orthographic match between un-diacritized forms Additional- orthographic matches as a result of relaxing a constraint 23

Outlines Introduction Arabic Morphological Ambiguity Problem The Proposed Disambiguation System Evaluation & Results Conclusions 24

Conclusions The ambiguity problem presents a challenge to ILTS The preferred method in ILTS for disambiguating multiple readings should consider the likelihood of an error and the difficulty of concepts If a large tagged learner corpus exist then the ambiguity problem can be resolved by considering the likelihood of errors 25

Thank you 26