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        <title>Improving HowNet-Based Chinese Word Sense Disambiguation with Translations</title>
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    <abstract>Word sense disambiguation (WSD) is the task of identifying the intended sense of a word in context. While prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, these resources have proven to be less effective for Chinese. Instead, the most widely used lexical knowledge base for Chinese is HowNet. Previous HowNet-based WSD methods have not exploited contextual translation information. In this paper, we present the first HowNet-based WSD system which combines monolingual contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. The results of our evaluation experiment on a test set from prior work demonstrate that our new method achieves a new state of the art for unsupervised Chinese WSD.</abstract>
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%0 Conference Proceedings
%T Improving HowNet-Based Chinese Word Sense Disambiguation with Translations
%A Zhang, Xiang
%A Hauer, Bradley
%A Kondrak, Grzegorz
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-etal-2022-improving-hownet
%X Word sense disambiguation (WSD) is the task of identifying the intended sense of a word in context. While prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, these resources have proven to be less effective for Chinese. Instead, the most widely used lexical knowledge base for Chinese is HowNet. Previous HowNet-based WSD methods have not exploited contextual translation information. In this paper, we present the first HowNet-based WSD system which combines monolingual contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. The results of our evaluation experiment on a test set from prior work demonstrate that our new method achieves a new state of the art for unsupervised Chinese WSD.
%R 10.18653/v1/2022.findings-emnlp.331
%U https://aclanthology.org/2022.findings-emnlp.331
%U https://doi.org/10.18653/v1/2022.findings-emnlp.331
%P 4530-4536

Markdown (Informal)

[Improving HowNet-Based Chinese Word Sense Disambiguation with Translations](https://aclanthology.org/2022.findings-emnlp.331) (Zhang et al., Findings 2022)

• Improving HowNet-Based Chinese Word Sense Disambiguation with Translations (Zhang et al., Findings 2022)

ACL

• Xiang Zhang, Bradley Hauer, and Grzegorz Kondrak. 2022. Improving HowNet-Based Chinese Word Sense Disambiguation with Translations. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4530–4536, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.