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Word Sense Induction (WSI) is the ability to automatically induce word sensesfrom corpora. The WSI task was first proposed to overcome the limitations ofmanually annotated corpus that are required in word sense disambiguationsystems. Even though several works have been proposed to induce word senses,existing systems are still very limited in the sense that they make use ofstructured, domain-specific knowledge sources. In this paper, we devise amethod that leverages recent findings in word embeddings research to generatecontext embeddings, which are embeddings containing information about thesemantical context of a word. In order to induce senses, we modeled the set ofambiguous words as a complex network. In the generated network, two instances(nodes) are connected if the respective context embeddings are similar. Uponusing well-established community detection methods to cluster the obtainedcontext embeddings, we found that the proposed method yields excellentperformance for the WSI task. Our method outperformed competing algorithms andbaselines, in a completely unsupervised manner and without the need of anyadditional structured knowledge source.

Word sense induction using word embeddings and community detection in complex networks