Evaluating entity linking with wikipedia

Ben Hachey*, Will Radford, Joel Nothman, Matthew Honnibal, James R. Curran

*Corresponding author for this work

Research output: Contribution to journalArticle

163 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)130-150
Number of pages21
JournalArtificial Intelligence
Volume194
DOIs
Publication statusPublished - Jan 2013

Keywords

  • Disambiguation
  • Information extraction
  • Named Entity Linking
  • Semi-structured resources
  • Wikipedia

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