katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English

Ekaterina Voskoboinik

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)
27 Downloads (Pure)

Abstract

This paper describes systems submitted to SemEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a nonlinear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of single word and multi-word expression (MWE) targets simultaneously. This approach proved to be the most beneficial for multi-word expression targets. We also demonstrate that some hand-crafted features differ in their importance for the target types.

Original languageEnglish
Title of host publicationSemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
EditorsAlexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
PublisherAssociation for Computational Linguistics
Pages700-705
Number of pages6
ISBN (Electronic)9781954085701
DOIs
Publication statusPublished - 2021
MoE publication typeA4 Conference publication
EventInternational Workshop on Semantic Evaluation - Virtual, Bangkok, Thailand
Duration: 5 Aug 20216 Aug 2021

Publication series

NameSemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop

Workshop

WorkshopInternational Workshop on Semantic Evaluation
Abbreviated titleSemEval
Country/TerritoryThailand
CityBangkok
Period05/08/202106/08/2021

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  • DigiTala: Aka-Digi Tala

    Kurimo, M., Al-Ghezi, R., Getman, Y. & Voskoboinik, E.

    01/09/201931/08/2023

    Project: Academy of Finland: Other research funding

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