Abstract
Named entity disambiguation has been one of the main challenges to research in Information Extraction and development of Semantic Web. Therefore, it has attracted much research effort, with various methods introduced for different domains, scopes, and purposes. In this paper, we propose a new approach that is not limited to some entity classes and does not require well-structured texts. The novelty is that it exploits relations between co-occurring entities in a text as defined in a knowledge base for disambiguation. Combined with class weighting and coreference resolution, our knowledge-based method outperforms KIM system in this problem. Implemented algorithms and conducted experiments for the method are presented and discussed.
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Nguyen, H.T., Cao, T.H. (2007). A Knowledge-Based Approach to Named Entity Disambiguation in News Articles. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_64
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DOI: https://doi.org/10.1007/978-3-540-76928-6_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
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