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
Venues:
ACL | IJCNLP | 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:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.70.pdf