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

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Conference article in proceedings
Date
2021
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Degree programme
Language
en
Pages
6
700-705
Series
SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
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.
Description
Funding Information: I would like to express my gratitude to the anonymous reviewer for their insightful and helpful comments. I also want thank Ragheb Al-Ghezi for proofreading and Mikko Kurimo for his comments on an earlier draft of the paper. Lastly, I want to say thank you to Science-IT team for providing computational environment used in the experiments. This work is a part of DigiTala project which is funded by the Academy of Finland (grant number 322625). Publisher Copyright: © 2021 Association for Computational Linguistics.
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Voskoboinik , E 2021 , katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English . in A Palmer , N Schneider , N Schluter , G Emerson , A Herbelot & X Zhu (eds) , SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop . SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop , Association for Computational Linguistics , pp. 700-705 , International Workshop on Semantic Evaluation , Bangkok , Thailand , 05/08/2021 . https://doi.org/10.18653/v1/2021.semeval-1.91