JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction

Chaya Liebeskind, Otniel Elkayam, Shmuel Liebeskind


Abstract
In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words
Anthology ID:
2021.semeval-1.13
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:
138–143
Language:
URL:
https://aclanthology.org/2021.semeval-1.13
DOI:
10.18653/v1/2021.semeval-1.13
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.13.pdf