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| Title: | PolyU CBS-Comp at SemEval-2021 Task 1 : Lexical Complexity Prediction (LCP) | Authors: | Xiang, R Gu, J Chersoni, E Li, W Lu, Q Huang, CR |
Issue Date: | 2021 | Source: | In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), p. 565-570. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL), 2021 | 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. | Publisher: | Association for Computational Linguistics (ACL) | ISBN: | 978-1-954085-70-1 | DOI: | 10.18653/v1/2021.semeval-1.70 | Description: | SemEval-2021: The 15th International Workshop on Semantic Evaluation, August 5-6, 2021, Bangkok, Thailand (online) | Rights: | ©2021 Association for Computational Linguistics This publication is licensed on a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) The following publication 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 is available at https://doi.org/10.18653/v1/2021.semeval-1.70. |
| Appears in Collections: | Conference Paper |
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