• Title of article

    Evaluating Entity Linking with Wikipedia Original Research Article

  • Author/Authors

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

  • Issue Information
    روزنامه با شماره پیاپی سال 2012
  • Pages
    21
  • From page
    130
  • To page
    150
  • 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.
  • Keywords
    disambiguation , Wikipedia , Information extraction , Named Entity Linking , Semi-structured resources
  • Journal title
    Artificial Intelligence
  • Serial Year
    2012
  • Journal title
    Artificial Intelligence
  • Record number

    1207939