Improving Arabic diacritization through syntactic analysis

Anas Shahrour, Salam Khalifa, Nizar Habash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an approach to Arabic automatic diacritization that integrates syntactic analysis with morphological tagging through improving the prediction of case and state features. Our best system increases the accuracy of word diacritization by 2.5% absolute on all words, and 5.2% absolute on nominals over a state-of-theart baseline. Similar increases are shown on the full morphological analysis choice.

Original languageEnglish (US)
Title of host publicationConference Proceedings - EMNLP 2015
Subtitle of host publicationConference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Pages1309-1315
Number of pages7
ISBN (Electronic)9781941643327
DOIs
StatePublished - 2015
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal
Duration: Sep 17 2015Sep 21 2015

Publication series

NameConference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Other

OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2015
CountryPortugal
CityLisbon
Period9/17/159/21/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Shahrour, A., Khalifa, S., & Habash, N. (2015). Improving Arabic diacritization through syntactic analysis. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 1309-1315). (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1152