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I-AIBS Institute for Artificial Intelligence and Biological Systems

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Presentation on theme: "I-AIBS Institute for Artificial Intelligence and Biological Systems"— Presentation transcript:

1 I-AIBS Institute for Artificial Intelligence and Biological Systems
Arabic Language Computing applied to the Quran - a PhD research project by Kais Dukes I-AIBS Institute for Artificial Intelligence and Biological Systems School of Computing University of Leeds

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3 The Challenge: An interdisciplinary approach to understanding the Quran
(1) Quranic Studies (3) Computational Linguistics (2) Traditional Arabic Linguistics

4 (1) What is the Quran? The last in a series of 5 religious texts
Holy Book Prophet Text Dated Suhuf Ibrahim (Scrolls) Abraham ? The Tawrat (Torah) Moses 1500 BCE? The Zabur (Psalms) David 1000 BCE? The Injil (Gospel) Jesus 1 CE The Quran Muhammad (PBUH) CE

5 The central religious text of Islam
(1) What is the Quran? The central religious text of Islam Classical Arabic, years ago All believers should learn the text; translations are “interpretations” Islamic Law (legal logic) Divine guidance & direction Science and philosophy Has inspired Algebra, Linguistics

6 (2) Traditional Arabic Linguistics
Originated in Arabs studying the language of the Quran (scientific analysis for at least 1000 years – a lot older than English language!): - Orthography (diacritics and vowelization) - Etymology (Semitic roots) - Morphology (derivation and inflection) - Syntax (origins of dependency grammar) - Discourse Analysis & Rhetoric - Semantics & Pragmatics

7 (3) Computational Linguistics
Quran is online, for keyword search BUT verse-by-verse translations are interpretations Muslims should access the “true” Classical Arabic source

8 (3) Computational Linguistics
- How far can we go? - Is an Artificial Intelligence system realistic? Example question-answering dialog system: Question How long should I breastfeed my child for? Answer Mothers should suckle their offspring for two years, if the father wishes to complete the term (The Holy Quran, Verse 2:233).

9 An AI approach to understanding the Quran
Central Hypothesis Augmenting the text of the Quran with rich annotation will lead to a more accurate AI system. - Prepare the data by annotating the Quran. - Use the data to build an AI system for concept search and question-answering.

10 Annotating the Quran Challenges
Orthography - Complex non-standard script Morphology (word structure) - Arabic is highly inflected, challenging to analyze Grammar - Phrase structure, dependency Semantics – Ontology of Entities and Concepts referred to by pronouns and nouns

11 Annotating the Quran Solutions
- Computing advances have made annotation possible, to high accuracy - Leverage existing resources from Traditional Arabic Grammar Machine-Learning annotation followed by manual verification - Community effort using online volunteers

12 Recent Advances: Orthography
An accurate digital copy of the Quran? Encoding Issues Missing diacritics Simplified script (not Uthmani) Windows code page 1256, not Unicode Google Search for verse (68:38) on Jan 21, 2008 shows many typos

13 Recent Advances: Orthography
Tanzil Project (http://tanzil.info) Stable version released May 2008 Uses Unicode XML encoding, including the special characters designed for the complex Arabic script of the Quran Manually verified to 100% accuracy by a group of experts who have memorized the entire text of the Quran

14 Recent Advances: Orthography
Java Quran API (http://jqurantree.org) (Dukes 2009) Java classes for querying the Tanzil XML of the Quran gives authentic script on web-pages

15 Recent Advances: Morphology
- Buckwalter Arabic Morphological Analyzer (Tim Buckwalter, 2002) Morphological Analysis of the Quran at the University of Haifa (Shuly Wintner, 2004) - Lexeme & feature based morphological representation of Arabic (Nizar Habash, 2006)

16 The Haifa Corpus (2004) Multiple analysis for each word (up to 5)
rbb+fa&l+Noun+Triptotic+Masc+Sg+Pron+Dependent+1P+Sg rbb+fa&l+Noun+Triptotic+Masc+Sg+Gen Not manually verified Authors reports an F-measure of 86% Non-standard annotation scheme not familiar to traditional Arabic linguists e.g. extracting a list of all verbs is non-trivial Arabic text is only encoded phonetically instead of using the original Arabic. e.g. searching for a specific root is not easy

17 The Quranic Arabic Corpus http://corpus.quran.com/
Kais Dukes Arabic Language Computing Applied to the Quran – PhD (part-time) word structure - colour-coded morphological analysis translation - word-for-word English translations grammar- dependency parse following Arabic tradition semantics – ontology of entities and concepts Machine Learning - annotations used for A.I. training Impact - dozens of researchers have collaborated/cited, and a million visitors have used the website this year

18 The Quranic Arabic Corpus Verified Uthmani Script
Unicode Uthmani Script Sourced from the verified Tanzil project

19 The Quranic Arabic Corpus Phonetics (faja'alnāhumu)
Phonetic transcription generated algorithmically Guided by Arabic vowelized diacritics

20 The Quranic Arabic Corpus Interlinear translation
Word-for-word translation from accepted sources Interlinear translation scheme

21 The Quranic Arabic Corpus Location Reference (21:70:4)
Common standard for verses (Chapter:Verse) Extended in the QAC corpus to include word numbers and segment numbers, e.g. (21:70:4:2)

22 The Quranic Arabic Corpus Morphological Segmentation
Division of a single word into multiple segments Part-of-speech tag assigned to each segment - Traditional Arabic Grammar rules used for division

23 The Quranic Arabic Corpus Morphological segment features

24 The Quranic Arabic Corpus Arabic Grammar Summary

25 The Quranic Arabic Treebank Syntactic Annotation
Dependency Grammar based onإعراب (i'rāb) Syntactico-semantic roles for each word

26 The Quranic Arabic Treebank Ontology of entities and concepts
linked to/from nouns and pronouns in the text

27 The Quranic Arabic Treebank Framework for collaboration
Message Board: “If you come across a word and you feel that a better analysis could be provided, you can suggest a correction online by clicking on an Arabic word” (currently 5228 resolved messages; 1048 under review) Resources: Publications; Citations, Reviews, FAQs, Feedback, Data Download, Software download, Mailing list

28 The Quranic Arabic Treebank Users: researchers, public
Artificial Intelligence and Computational Linguistics Arabic linguistics Quranic and Islamic Studies Classical literature analysis Anyone who wants to appreciate the Quran

29 The Quranic Arabic Treebank new Computational Linguistics?
First Treebank of Classical Arabic Free Treebank of the Quran First formal representation of Traditional Arabic Grammar using constituency/dependency graphs Machine-Learning parser

30 The Quranic Arabic Corpus Part-of-speech Tagging
Part-of-speech tags adapted from Traditional Arabic Grammar, and mapped to English equivalents (not the other way around) These tags apply to words in the Quran, as well as to individual morphological segments in the text Part-of-speech Tag Name Arabic Name N Noun اسم PN Proper noun اسماء علم PRON Personal pronoun ضمير DEM Demonstrative pronoun اسم اشارة REL Relative pronoun اسم موصول ADJ Adjective صفة V Verb فعل P Preposition حرف جر PART Particle حرف INTG Interrogative particle حرف استفهام VOC Vocative particle حرف نداء NEG Negative particle حرف نفي FUT Future particle حرف استقبال CONJ Conjunction حرف عطف NUM Number رقم T Time adverb ظرف زمان LOC Location adverb ظرف مكان EMPH Emphatic lām prefix لام التوكيد PRP Purpose lām prefix لام التعليل IMPV Imperative lām prefix لام الامر INL Quranic initials حروف مقطعة

31 Automatic Annotation Classical Arabic Dependency Parser
Joakim Nivre (2009) dependency parsing using a shift/reduce queue/stack architecture with machine learning Following similar architecture, but with hand written rules, custom parser has an F-measure of 77.2%

32 University of Leeds Postgraduate Researcher Conference 2011
Criteria for “PGR Researcher of the Year 2011” Ability to communicate research to the lay and non-specialist research audience Impact/potential impact of the research in terms of e.g. application of findings for economic or social benefit; the significance of the contribution/potential contribution of the research to the academic subject area Evidence of local or national publicity or public engagement.

33 Ability to communicate research to the lay and non-specialist audience
Example Feedback (319 comments) “I would like to applaud you for your effort” Prof Behnam Sadeghi, Stanford University “We are big admirers of the work” Prof Gregory Crane, Classics Dept, Tufts University “I regularly use your work on the Qur'an and read it whenever I can.” Prof Yousuf Islam, Director, Daffodil International University “Congratulations to all concerned on this project” - Prof Michael Arthur, VC, Leeds Uni

34 Impact: application of findings for economic or social benefit
Over a million users already, and growing; many unforseen social benefits, eg: “I work as a chaplain in correctional centers in the State of Missouri, U.S.A. Thanks for your permission to use the Quranic Arabic Corpus in these correctional centers” Tadar Wazir.

35 Impact: significance of the research to the academic subject area
10 papers in research conferences & journals 25 citations (from Google Scholar) - so far... Positive feedback from top researchers Only free-to-download Arabic treebank A de-facto standard data-set for AI research

36 Evidence of local or national publicity or public engagement
Newspapers, eg Muslim Post; better still: Website – world-wide public engagement!

37 I-AIBS Institute for Artificial Intelligence and Biological Systems
Conclusion This is not the end to come: 2nd half of PhD project; and more? Kais Dukes I-AIBS Institute for Artificial Intelligence and Biological Systems School of Computing University of Leeds


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