Word sense induction for novel sense detection

  • Authors:

  • Jey Han Lau;Paul Cook;Diana McCarthy;David Newman;Timothy Baldwin

  • Affiliations:

  • NICTA Victoria Research Laboratory and University of Melbourne;University of Melbourne;Lexical Computing;University of California Irvine;NICTA Victoria Research Laboratory and University of Melbourne

  • Venue:
  • EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
  • Year:
  • 2012

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Abstract

We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically detect words with emergent novel senses, as well as token occurrences of those senses. We start by exploring the utility of standard topic models for word sense induction (WSI), with a pre-determined number of topics (=senses). We next demonstrate that a non-parametric formulation that learns an appropriate number of senses per word actually performs better at the WSI task. We go on to establish state-of-the-art results over two WSI datasets, and apply the proposed model to a novel sense detection task.