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

Katja Voskoboinik


Abstract
This paper describes systems submitted to Se- mEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a non-linear 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.
Anthology ID:
2021.semeval-1.91
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
700–705
Language:
URL:
https://aclanthology.org/2021.semeval-1.91
DOI:
10.18653/v1/2021.semeval-1.91
Bibkey:
Cite (ACL):
Katja Voskoboinik. 2021. katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 700–705, Online. Association for Computational Linguistics.
Cite (Informal):
katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English (Voskoboinik, SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.91.pdf