Structural Semantic Relatedness: A knowledge-based method to named entity disambiguation
|Structural Semantic Relatedness: A knowledge-based method to named entity disambiguation|
|Author(s)||Han X., Zhao J.|
|Published in||ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: AS graph, Complex structure, High quality, Knowledge sources, Knowledge-based methods, Named entities, Semantic knowledge, Social Networks, Structural semantics, Wikipedia, Wordnet, Computational linguistics, Knowledge based systems, Knowledge management, Semantics, Natural language processing systems)|
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Structural Semantic Relatedness: A knowledge-based method to named entity disambiguation is a 2010 conference paper written in English by Han X., Zhao J. and published in ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference.
Name ambiguity problem has raised urgent demands for efficient, high-quality named entity disambiguation methods. In recent years, the increasing availability of large-scale, rich semantic knowledge sources (such as Wikipedia and WordNet) creates new opportunities to enhance the named entity disambiguation by developing algorithms which can exploit these knowledge sources at best. The problem is that these knowledge sources are heterogeneous and most of the semantic knowledge within them is embedded in complex structures, such as graphs and networks. This paper proposes a knowledge-based method, called Structural Semantic Relatedness (SSR), which can enhance the named entity disambiguation by capturing and leveraging the structural semantic knowledge in multiple knowledge sources. Empirical results show that, in comparison with the classical BOW based methods and social network based methods, our method can significantly improve the disambiguation performance by respectively 8.7% and 14.7%.
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