Statistical denormalization for arabic text

Mohammed Moussa, Mohamed Waleed Fakhr, Kareem Darwish

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

4 Citations (Scopus)

Abstract

In this paper, we focus on a sub-problem of Arabic text error correction, namely Arabic Text Denormalization. Text Denormalization is considered an important post-processing step when performing machine translation into Arabic. We examine different approaches for denormalization via the use of language modeling, stemming, and sequence labeling. We show the effectiveness of different approaches and how they can be combined to attain better results. We perform intrinsic evaluation as well as extrinsic evaluation in the context of machine translation.

Original languageEnglish
Title of host publication11th Conference on Natural Language Processing, KONVENS 2012: Empirical Methods in Natural Language Processing - Proceedings of the Conference on Natural Language Processing 2012
Pages228-232
Number of pages5
Volume5
Publication statusPublished - 1 Dec 2012
Event11th Conference on Natural Language Processing 2012: Empirical Methods in Natural Language Processing, KONVENS 2012 - Vienna, Austria
Duration: 19 Sep 201221 Sep 2012

Other

Other11th Conference on Natural Language Processing 2012: Empirical Methods in Natural Language Processing, KONVENS 2012
CountryAustria
CityVienna
Period19/9/1221/9/12

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ASJC Scopus subject areas

  • Software

Cite this

Moussa, M., Fakhr, M. W., & Darwish, K. (2012). Statistical denormalization for arabic text. In 11th Conference on Natural Language Processing, KONVENS 2012: Empirical Methods in Natural Language Processing - Proceedings of the Conference on Natural Language Processing 2012 (Vol. 5, pp. 228-232)