@inproceedings{yao-etal-2021-connect,
title = "Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories",
author = "Yao, Wenlin and
Pan, Xiaoman and
Jin, Lifeng and
Chen, Jianshu and
Yu, Dian and
Yu, Dong",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.610",
doi = "10.18653/v1/2021.emnlp-main.610",
pages = "7741--7751",
abstract = "Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2{\%} on the All-Words WSD Task and 4.3{\%} on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.",
}
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<abstract>Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.</abstract>
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%0 Conference Proceedings
%T Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
%A Yao, Wenlin
%A Pan, Xiaoman
%A Jin, Lifeng
%A Chen, Jianshu
%A Yu, Dian
%A Yu, Dong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F yao-etal-2021-connect
%X Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.
%R 10.18653/v1/2021.emnlp-main.610
%U https://aclanthology.org/2021.emnlp-main.610
%U https://doi.org/10.18653/v1/2021.emnlp-main.610
%P 7741-7751
Markdown (Informal)
[Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories](https://aclanthology.org/2021.emnlp-main.610) (Yao et al., EMNLP 2021)
ACL