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

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

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

4. 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

5. 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

6. 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

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

8. 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

9. 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

10. 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

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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

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

22. 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

23. 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

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

25. 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

26. Thank you 26