Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet

Proceedings of the 28th International Conference on Computational Linguistics, pages 1752–1757 Barcelona, Spain (Online), December 8-13, 2020 1752 Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet Bairu Hou23∗, Fanchao Qi12∗, Yuan Zang12∗, Xurui Zhang12, Zhiyuan Liu12†, Maosong Sun12 1Department of Computer Science and Technology, Tsinghua University 2Institute for Artificial Intelligence, Tsinghua University Beijing National Research Center for Information Science and Technology 3School of Economics and Management, Tsinghua University houbr.17@sem.tsinghua.edu.cn {zangy17,qfc17,zhangxr16}@mails.tsinghua.edu.cn {liuzy,sms}@tsinghua.edu.cn Abstract Word sense disambiguation (WSD) is a fundamental natural language processing task. Unsu- pervised knowledge-based WSD only relies on a lexical knowledge base as the sense inven- tory and has wider practical use than supervised WSD that requires a mass of sense-annotated data. HowNet is the most widely used lexical knowledge base in Chinese WSD. Because of its uniqueness, however, most of existing unsupervised WSD methods cannot work for HowNet- based WSD, and the tailor-made methods have not obtained satisfying results. In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models. In experiments, considering ex- isting evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset. Experimental results demonstrate that our model achieves significantly better performance than all the baseline methods. All the code and data of this paper are available at https://github.com/thunlp/SememeWSD. 1 Introduction Word sense disambiguation (WSD) is a long-standing natural language processing task which aims to identify the correct sense of a polysemous word in the context (Navigli, 2009). WSD is fundamental to natural language understanding (Navigli, 2018) and has been proven to be beneficial to many other tasks such as machine translation (Vickrey et al., 2005; Pu et al., 2018), information extraction (Bovi et al., 2015) and information retrieval (Zhong and Ng, 2012). There are two main kinds of WSD, namely supervised disambiguation and unsupervised knowledge- based disambiguation. Supervised WSD requires large amounts of sense-annotated training corpora that are difficult to obtain (Tripodi and Navigli, 2019). In contrast, unsupervised knowledge-based WSD relies on only an external lexical knowledge base (LKB) as the sense inventory and thus has wider practical use. Existing unsupervised knowledge-based WSD approaches mainly comprise gloss-based and graph- based methods. The gloss-based methods utilize glosses (sense definitions) to conduct disambiguation. Lesk algorithm (Lesk, 1986) is a seminal gloss-based method which disambiguates a word by selecting the sense whose gloss overlaps most with the context. There are many subsequent methods based on Lesk algorithm (Banerjee and Pedersen, 2003; Basile et al., 2014; Wang et al., 2020). The graph-based methods is the other major type of knowledge-based WSD approaches, which exploit the structures of the LKB for disambiguation (Agirre et al., 2014; Moro et al., 2014; Chaplot et al., 2015; Chaplot and Salakhutdinov, 2018). Besides, a recent method uses both glosses and structural information of LKBs in knowledge-based WSD and achieves state-of-the-art performance (Scarlini et al., 2020). In Chinese WSD, HowNet (Dong and Dong, 2006) is the most widely used LKB …

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