LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures

Yves Bestgen


Abstract
This paper describes the system developed by the Laboratoire d’analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent.
Anthology ID:
2021.semeval-1.71
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
571–577
Language:
URL:
https://aclanthology.org/2021.semeval-1.71
DOI:
10.18653/v1/2021.semeval-1.71
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.71.pdf