archer at SemEval-2021 Task 1: Contextualising Lexical Complexity

Irene Russo


Abstract
Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to assess single words lexical complexity, combining linguistic and psycholinguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word’s in-context complexity.
Anthology ID:
2021.semeval-1.90
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Editors:
Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
694–699
Language:
URL:
https://aclanthology.org/2021.semeval-1.90
DOI:
10.18653/v1/2021.semeval-1.90
Bibkey:
Cite (ACL):
Irene Russo. 2021. archer at SemEval-2021 Task 1: Contextualising Lexical Complexity. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 694–699, Online. Association for Computational Linguistics.
Cite (Informal):
archer at SemEval-2021 Task 1: Contextualising Lexical Complexity (Russo, SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.90.pdf
Data
Visual Genome