A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment

Word sense induction (WSI) seeks to automat-ically discover the senses of a word in a cor-pus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent vari-ables to be inferred jointly. Topics are in-formed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsu-pervised ways of enriching the original cor-pus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant im-provements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.