Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction

Alejandro Mosquera


Abstract
This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out of 48 systems for sub-task 1 and achieved Pearson correlation coefficients of 0.779 and 0.809 for single words and multi-word expressions respectively. The conclusion is that a combination of lexical, contextual and semantic features can still produce strong baselines when compared against human judgement.
Anthology ID:
2021.semeval-1.68
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:
554–559
Language:
URL:
https://aclanthology.org/2021.semeval-1.68
DOI:
10.18653/v1/2021.semeval-1.68
Bibkey:
Cite (ACL):
Alejandro Mosquera. 2021. Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 554–559, Online. Association for Computational Linguistics.
Cite (Informal):
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction (Mosquera, SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.68.pdf