RESLVE: leveraging user interest to improve entity disambiguation on short text

  • Authors:

  • Elizabeth L. Murnane;Bernhard Haslhofer;Carl Lagoze

  • Affiliations:

  • Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;University of Michigan, Ann Arbor, MI, USA

  • Venue:
  • Proceedings of the 22nd international conference on World Wide Web companion
  • Year:
  • 2013

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Abstract

We address the Named Entity Disambiguation (NED) problem for short, user-generated texts on the social Web. In such settings, the lack of linguistic features and sparse lexical context result in a high degree of ambiguity and sharp performance drops of nearly 50% in the accuracy of conventional NED systems. We handle these challenges by developing a model of user-interest with respect to a personal knowledge context; and Wikipedia, a particularly well-established and reliable knowledge base, is used to instantiate the procedure. We conduct systematic evaluations using individuals' posts from Twitter, YouTube, and Flickr and demonstrate that our novel technique is able to achieve substantial performance gains beyond state-of-the-art NED methods.