Noise-robust morphological disambiguation for dialectal arabic

Nasser Zalmout, Alexander Erdmann, Nizar Habash

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

Abstract

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is characterized with morpho-syntactic and phonetic variations. These issues increase sparsity in NLP models and reduce accuracy. We present a neural morphological tagging and disambiguation model for Egyptian Arabic, with various extensions to handle noisy and inconsistent content. Our models achieve about 5% relative error reduction (1.1% absolute improvement) for full morphological analysis, and around 22% relative error reduction (1.8% absolute improvement) for part-of-speech tagging, over a state-of-The-Art baseline.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages953-964
Number of pages12
ISBN (Electronic)9781948087278
StatePublished - Jan 1 2018
Event2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
Duration: Jun 1 2018Jun 6 2018

Publication series

NameNAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
CountryUnited States
CityNew Orleans
Period6/1/186/6/18

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

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  • Cite this

    Zalmout, N., Erdmann, A., & Habash, N. (2018). Noise-robust morphological disambiguation for dialectal arabic. In Long Papers (pp. 953-964). (NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL).