PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)

Rong Xiang, Jinghang Gu, Emmanuele Chersoni, Wenjie Li, Qin Lu, Chu-Ren Huang


Abstract
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context. Our top system, based on a combination of lexical, syntactic, word embeddings and Transformers-derived features and on a Gradient Boosting Regressor, achieves a top correlation score of 0.754 on the subtask 1 for single words and 0.659 on the subtask 2 for multiword expressions.
Anthology ID:
2021.semeval-1.70
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:
565–570
Language:
URL:
https://aclanthology.org/2021.semeval-1.70
DOI:
10.18653/v1/2021.semeval-1.70
Bibkey:
Cite (ACL):
Rong Xiang, Jinghang Gu, Emmanuele Chersoni, Wenjie Li, Qin Lu, and Chu-Ren Huang. 2021. PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP). In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 565–570, Online. Association for Computational Linguistics.
Cite (Informal):
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP) (Xiang et al., SemEval 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2021.semeval-1.70.pdf