Evaluating Entity Linking with Wikipedia

  • Authors:

  • Ben Hachey;Will Radford;Joel Nothman;Matthew Honnibal;James R. Curran

  • Affiliations:

  • Research & Development, Thomson Reuters Corporation, St. Paul, MN 55123, USA;School of Information Technologies, University of Sydney, NSW 2006, Australia and Capital Markets CRC, 55 Harrington Street, NSW 2000, Australia;School of Information Technologies, University of Sydney, NSW 2006, Australia and Capital Markets CRC, 55 Harrington Street, NSW 2000, Australia;Department of Computing, Macquarie University, NSW 2109, Australia;School of Information Technologies, University of Sydney, NSW 2006, Australia and Capital Markets CRC, 55 Harrington Street, NSW 2000, Australia

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2013

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Abstract

Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate entities and then disambiguate them, returning either the best candidate or nil. However, comparison has focused on disambiguation accuracy, making it difficult to determine how search impacts performance. Furthermore, important approaches from the literature have not been systematically compared on standard data sets. We reimplement three seminal nel systems and present a detailed evaluation of search strategies. Our experiments find that coreference and acronym handling lead to substantial improvement, and search strategies account for much of the variation between systems. This is an interesting finding, because these aspects of the problem have often been neglected in the literature, which has focused largely on complex candidate ranking algorithms.