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Quranic Arabic Corpus Data Mining & Text Analytics By Ismail Teladia & Abdullah Alazwari.

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Presentation on theme: "Quranic Arabic Corpus Data Mining & Text Analytics By Ismail Teladia & Abdullah Alazwari."— Presentation transcript:

1 Quranic Arabic Corpus Data Mining & Text Analytics By Ismail Teladia & Abdullah Alazwari

2 Introduction  What is the Quran ?  Holy book for Muslims  Revealed from 610 AD  6,236 verses, 114 chapters  Corpus Definition.  Written or spoken language  What is the Quranic Arabic Corpus ?  77,430 words of Quranic Arabic  Researcher: Kais Dukes

3 Features of QAC:  Morphological Annotation  Syntactic Treebank  Semantic Ontology

4 Morphological Annotation  Word By Word  Grammar  Syntax  Morphology  Part-of-speech tagging  Natural Language Computing Technology

5 Details of Word’s Grammar  Clicking the word gives more detail:  Type of Word  Translation  Gender  Case  Root  In addition it shows the verse in which word appears and sound recitation of the verse.

6 Syntactic Treebank  Verse by verse dependency graphs  Meaning of verse (broken down)  Sentence structure (dependencies)  Case  Mathematical graph theory

7 Ontology of Concepts  Knowledge representation  Relationship between concepts  Historic places and people  Named entity tagging  E.g. Sun, Moon, Star, Earth classified under “Astronomical Body”  Uses predicate logic

8 Visual Representation of Ontology  300 linked concepts with 350 relations

9 Conclusion  Uses of the QAC:  Analysing Arabic text of each verse  Linking Arabic words through dependencies  Finding relationships between concepts  Website used daily by 2,500 people from 165 countries

10 Map Showing Usage of QAC

11 Bibliography 

12 Thank you for listening!


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