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Word sense induction (WSI) is a difficult problem in natural languageprocessing that involves the unsupervised automatic detection of a word'ssenses (i.e. meanings). Recent work achieves significant results on the WSItask by pre-training a language model that can exclusively disambiguate wordsenses, whereas others employ previously pre-trained language models inconjunction with additional strategies to induce senses. In this paper, wepropose a novel unsupervised method based on hierarchical clustering andinvariant information clustering (IIC). The IIC is used to train a small modelto optimize the mutual information between two vector representations of atarget word occurring in a pair of synthetic paraphrases. This model is laterused in inference mode to extract a higher quality vector representation to beused in the hierarchical clustering. We evaluate our method on two WSI tasksand in two distinct clustering configurations (fixed and dynamic number ofclusters). We empirically demonstrate that, in certain cases, our approachoutperforms prior WSI state-of-the-art methods, while in others, it achieves acompetitive performance.

Word Sense Induction with Hierarchical Clustering and Mutual Information Maximization