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Structural semantic relatedness: A knowledge-based method to named entity disambiguation
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| Venue: | In Proceedings of the Annual Meeting of the ACL |
| Citations: | 9 - 2 self |
BibTeX
@INPROCEEDINGS{Han_structuralsemantic,
author = {Xianpei Han and Jun Zhao},
title = {Structural semantic relatedness: A knowledge-based method to named entity disambiguation},
booktitle = {In Proceedings of the Annual Meeting of the ACL},
year = {}
}
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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%. 1
Keyphrases
entity disambiguation structural semantic relatedness knowledge-based method knowledge source complex structure structural semantic knowledge entity disambiguation method rich semantic knowledge source recent year disambiguation performance social network empirical result urgent demand name ambiguity problem multiple knowledge source classical bow semantic knowledge new opportunity
