Neural context embeddings for automatic discovery of word senses

Mikael Kågebäck, Fredrik Johansson, Richard Johansson, Devdatt Dubhashi, (NAACL 2015 workshop on Vector Space Modeling for NLP)
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Word sense induction (WSI) is the problem of automatically building an inventory of senses for a set of target words using only a text corpus. We introduce a new method for embedding word instances and their context, for use in WSI. The method, Instance-context embedding (ICE), leverages neural word embeddings, and the correlation statistics they capture, to compute high quality embeddings of word contexts. In WSI, these context embeddings are clustered to find the word senses present in the text. ICE is based on a novel method for combining word embeddings using continuous Skip-gram, based on both semantic and a temporal aspects of context words. ICE is evaluated both in a new system, and in an extension to a previous system for WSI. In both cases, we surpass previous state-of-the-art, on the WSI task of SemEval- 2013, which highlights the generality of ICE. Our proposed system achieves a 33% relative improvement.

Source code now available here!


  title={Neural context embeddings for automatic discovery of word senses},
  author={K{\aa}geb{\"a}ck, Mikael and Johansson, Fredrik and Johansson, Richard and Dubhashi, Devdatt},
  booktitle={Proceedings of NAACL-HLT},

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