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
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