The role of documents vs. queries in extracting class attributes from text

  • Authors:
  • Marius Paşca;Benjamin Van Durme;Nikesh Garera

  • Affiliations:
  • Google Inc., Mountain View, CA;University of Rochester, Rochester, NY;Johns Hopkins University, Baltimore, MD

  • Venue:
  • Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
  • Year:
  • 2007

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Abstract

Challenging the implicit reliance on document collections, this paper discusses the pros and cons of using query logs rather than document collections, as self-contained sources of data in textual information extraction. The differences are quantified as part of a large-scale study on extracting prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45% more accurate on average, when acquired from query logs rather than Web documents.