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An established method for Word Sense Induction (WSI) uses a language model topredict probable substitutes for target words, and induces senses by clusteringthese resulting substitute vectors.  We replace the ngram-based language model (LM) with a recurrent one. Beyondbeing more accurate, the use of the recurrent LM allows us to effectively queryit in a creative way, using what we call dynamic symmetric patterns.  The combination of the RNN-LM and the dynamic symmetric patterns results instrong substitute vectors for WSI, allowing to surpass the currentstate-of-the-art on the SemEval 2013 WSI shared task by a large margin.

Word Sense Induction with Neural biLM and Symmetric Patterns