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