Unsupervised methods for determining object and relation synonyms on the web

  • Authors:
  • Alexander Yates;Oren Etzioni

  • Affiliations:
  • Computer and Information Sciences, Temple University, Philadelphia, PA;Computer Science and Engineering, University of Washington, Seattle, WA

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2009

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Abstract

The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fully-implemented system that runs in O(KN log N) time in the number of extractions, N, and the maximum number of synonyms per word, K. The system, called RESOLVER, introduces a probabilistic relational model for predicting whether two strings are co-referential based on the similarity of the assertions containing them. On a set of two million assertions extracted from the Web, RESOLVER resolves objects with 78% precision and 68% recall, and resolves relations with 90% precision and 35% recall. Several variations of RESOLVER's probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic RESOLVER system allows it to handle polysemous names with 97% precision and 95% recall on a data set from the TREC corpus.