@inproceedings{5809bcc7a11c4717a05ea6b2a7ab2343,
title = "PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)",
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.",
author = "Rong Xiang and Jinghang Gu and Emmanuele Chersoni and Wenjie Li and Qin Lu and Chu-ren Huang",
note = "Funding Information: We acknowledge GRF grant (CERG PolyU 15211/14E, PolyU 152006/16E, PolyU YW4H), The Hong Kong Polytechnic University Postdoctoral Fellowships Scheme Projects. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 15th International Workshop on Semantic Evaluation, SemEval-2021 ; Conference date: 05-08-2021 Through 06-08-2021",
year = "2021",
month = aug,
doi = "10.18653/v1/2021.semeval-1.70",
language = "English",
series = "SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "565--570",
editor = "Alexis Palmer and Nathan Schneider and Natalie Schluter and Guy Emerson and Aurelie Herbelot and Xiaodan Zhu",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
address = "United States",
}