Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet
Bairu Hou, Fanchao Qi, Yuan Zang, Xurui Zhang, Zhiyuan Liu, Maosong Sun · 2020
Word sense disambiguation (WSD) is a fundamental natural language processing task.Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory 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 HowNetbased 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language model task of pre-trained language models.In experiments, considering existing evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset.Experimental results demonstrate that our model achieves significantly better performance than all the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.