Personalizing Lexical Simplification

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationCOLING 2018 : The 27th International Conference on Computational Linguistics
Subtitle of host publicationProceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages224-232
ISBN (Electronic)9781948087506
Publication statusPublished - Aug 2018

Conference

TitleThe 27th International Conference on Computational Linguistics (COLING 2018)
LocationSanta Fe Community Convention Center
PlaceUnited States
CityNew Mexico
Period20 - 26 August 2018

Abstract

Given an input text from the user, a lexical simplification (LS) system makes the text easier to understand by substituting difficult words with simpler words. The best substitution may vary from one user to another, given individual differences in vocabulary proficiency level. Most current systems, however, do not consider these variations, and are instead trained to find one optimal substitution or list of substitutions for all users. This paper measures the benefits of using complex word identification (CWI) models to personalize an LS system. Experimental results show that even a simple CWI model, based on graded vocabulary lists, can help reduce the number of unnecessary simplifications and complex words in the output for learners of English at different proficiency levels.

Citation Format(s)

Personalizing Lexical Simplification. / Lee, John; Yeung, Chak Yan.
COLING 2018 : The 27th International Conference on Computational Linguistics: Proceedings of the Conference. Association for Computational Linguistics (ACL), 2018. p. 224-232.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review