@inproceedings{song-etal-2021-improved-word,
title = "Improved Word Sense Disambiguation with Enhanced Sense Representations",
author = "Song, Yang and
Ong, Xin Cai and
Ng, Hwee Tou and
Lin, Qian",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.365",
doi = "10.18653/v1/2021.findings-emnlp.365",
pages = "4311--4320",
abstract = "Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the proposed enhancements, our system achieves an F1 score of 82.0{\%} on the standard benchmark test dataset of the English all-words WSD task, surpassing all previous published scores on this benchmark dataset.",
}
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<abstract>Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the proposed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing all previous published scores on this benchmark dataset.</abstract>
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%0 Conference Proceedings
%T Improved Word Sense Disambiguation with Enhanced Sense Representations
%A Song, Yang
%A Ong, Xin Cai
%A Ng, Hwee Tou
%A Lin, Qian
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F song-etal-2021-improved-word
%X Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the proposed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing all previous published scores on this benchmark dataset.
%R 10.18653/v1/2021.findings-emnlp.365
%U https://aclanthology.org/2021.findings-emnlp.365
%U https://doi.org/10.18653/v1/2021.findings-emnlp.365
%P 4311-4320
Markdown (Informal)
[Improved Word Sense Disambiguation with Enhanced Sense Representations](https://aclanthology.org/2021.findings-emnlp.365) (Song et al., Findings 2021)
ACL