2010 StructuralSemanticRelatednessaK

(Han et al., 2010) ⇒ Xianpei Han, and Jun Zhao. (2010). “Structural Semantic Relatedness: A Knowledge-based Method to Named Entity Disambiguation.” In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.

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Abstract

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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 StructuralSemanticRelatednessaK	Xianpei Han Jun Zhao			Structural Semantic Relatedness: A Knowledge-based Method to Named Entity Disambiguation