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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.semeval-1.70.pdf