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Topic Modeling for Word Sense Induction

  • Johannes Knopp
  • Johanna Völker
  • Simone Paolo Ponzetto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8105)

Abstract

In this paper, we present a novel approach to Word Sense Induction which is based on topic modeling. Key to our methodology is the use of word-topic distributions as a means to estimate sense distributions. We provide these distributions as input to a clustering algorithm in order to automatically distinguish between the senses of semantically ambiguous words. The results of our evaluation experiments indicate that the performance of our approach is comparable to state-of-the-art methods whose sense distinctions are not as easily interpretable.

Keywords

word sense induction topic models lexical semantics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Johannes Knopp
    • 1
  • Johanna Völker
    • 1
  • Simone Paolo Ponzetto
    • 1
  1. 1.Data & Web Science Research GroupUniversity of MannheimGermany

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