Personalizing Lexical Simplification

John Lee; Chak Yan Yeung

Personalizing Lexical Simplification

Department of Linguistics and Translation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

40 Citations (Scopus)

72 Downloads (CityUHK Scholars)

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. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

Original language	English
Title of host publication	Proceedings of the 27th International Conference on Computational Linguistics
Publisher	Association for Computational Linguistics
Pages	224-232
ISBN (Print)	9781948087506
Publication status	Published - Aug 2018
Event	The 27th International Conference on Computational Linguistics (COLING 2018) - Santa Fe Community Convention Center, New Mexico, United States Duration: 20 Aug 2018 → 26 Aug 2018 http://coling2018.org/ http://aclweb.org/anthology/C18-1019 https://aclanthology.coli.uni-saarland.de/volumes/proceedings-of-the-27th-international-conference-on-computational-linguistics https://aclanthology.coli.uni-saarland.de/papers/C18-1292/c18-1292 https://aclanthology.org/C18-1

Publication series

Name	COLING - International Conference on Computational Linguistics, Proceedings

Conference

Conference	The 27th International Conference on Computational Linguistics (COLING 2018)
Abbreviated title	COLING 2018
Place	United States
City	New Mexico
Period	20/08/18 → 26/08/18
Internet address	http://coling2018.org/ http://aclweb.org/anthology/C18-1019 https://aclanthology.coli.uni-saarland.de/volumes/proceedings-of-the-27th-international-conference-on-computational-linguistics https://aclanthology.coli.uni-saarland.de/papers/C18-1292/c18-1292 https://aclanthology.org/C18-1

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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@inproceedings{4e880e35839e49928caf32b1a2e571ae,

title = "Personalizing Lexical Simplification",

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. {\textcopyright} 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.",

author = "John Lee and Yeung, \{Chak Yan\}",

year = "2018",

month = aug,

language = "English",

isbn = "9781948087506",

series = "COLING - International Conference on Computational Linguistics, Proceedings",

publisher = "Association for Computational Linguistics",

pages = "224--232",

booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",

address = "United States",

note = "The 27th International Conference on Computational Linguistics (COLING 2018) , COLING 2018 ; Conference date: 20-08-2018 Through 26-08-2018",

url = "http://coling2018.org/, http://aclweb.org/anthology/C18-1019, https://aclanthology.coli.uni-saarland.de/volumes/proceedings-of-the-27th-international-conference-on-computational-linguistics, https://aclanthology.coli.uni-saarland.de/papers/C18-1292/c18-1292, https://aclanthology.org/C18-1",

}

Lee, J & Yeung, CY 2018, Personalizing Lexical Simplification. in Proceedings of the 27th International Conference on Computational Linguistics. COLING - International Conference on Computational Linguistics, Proceedings, Association for Computational Linguistics, pp. 224-232, The 27th International Conference on Computational Linguistics (COLING 2018) , New Mexico, United States, 20/08/18. <https://aclanthology.org/C18-1019/>

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

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Personalizing Lexical Simplification

AU - Lee, John

AU - Yeung, Chak Yan

PY - 2018/8

Y1 - 2018/8

N2 - 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. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

AB - 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. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781948087506

T3 - COLING - International Conference on Computational Linguistics, Proceedings

SP - 224

EP - 232

BT - Proceedings of the 27th International Conference on Computational Linguistics

PB - Association for Computational Linguistics

T2 - The 27th International Conference on Computational Linguistics (COLING 2018)

Y2 - 20 August 2018 through 26 August 2018

ER -

Personalizing Lexical Simplification

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