Extracting key phrases to disambiguate personal name queries in web search

  • Authors:

  • Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Affiliations:

  • The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan

  • Venue:
  • CLIIR '06 Proceedings of the Workshop on How Can Computational Linguistics Improve Information Retrieval?
  • Year:
  • 2006

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Abstract

Assume that you are looking for information about a particular person. A search engine returns many pages for that person's name. Some of these pages may be on other people with the same name. One method to reduce the ambiguity in the query and filter out the irrelevant pages, is by adding a phrase that uniquely identifies the person we are interested in from his/her namesakes. We propose an unsupervised algorithm that extracts such phrases from the Web. We represent each document by a term-entity model and cluster the documents using a contextual similarity metric. We evaluate the algorithm on a dataset of ambiguous names. Our method outperforms baselines, achieving over 80% accuracy and significantly reduces the ambiguity in a web search task.