@inproceedings{lee-yeung-2019-personalized,
title = "Personalized Substitution Ranking for Lexical Simplification",
author = "Lee, John and
Yeung, Chak Yan",
editor = "van Deemter, Kees and
Lin, Chenghua and
Takamura, Hiroya",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8634",
doi = "10.18653/v1/W19-8634",
pages = "258--267",
abstract = "A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user{'}s vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words.",
}
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%0 Conference Proceedings
%T Personalized Substitution Ranking for Lexical Simplification
%A Lee, John
%A Yeung, Chak Yan
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F lee-yeung-2019-personalized
%X A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user’s vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words.
%R 10.18653/v1/W19-8634
%U https://aclanthology.org/W19-8634
%U https://doi.org/10.18653/v1/W19-8634
%P 258-267
Markdown (Informal)
[Personalized Substitution Ranking for Lexical Simplification](https://aclanthology.org/W19-8634) (Lee & Yeung, INLG 2019)
ACL