Structural Semantic Relatedness: A Knowledge-Based Method to Named Entity Disambiguation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 50–59, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Structural Semantic Relatedness: A Knowledge-Based Method to Named Entity Disambiguation Xianpei Han Jun Zhao∗ National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing 100190, China {xphan,jzhao}@nlpr.ia.ac.cn ∗ Corresponding author Abstract Name ambiguity problem has raised urgent demands for efficient, high-quality named ent- ity disambiguation methods. In recent years, the increasing availability of large-scale, rich semantic knowledge sources (such as Wikipe- dia 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 en- hance the named entity disambiguation by capturing and leveraging the structural seman- tic 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 per- formance by respectively 8.7% and 14.7%. 1 Introduction Name ambiguity problem is common on the Web. For example, the name “Michael Jordan” represents more than ten persons in the Google search results. Some of them are shown below: Michael (Jeffrey) Jordan, Basketball Player Michael (I.) Jordan, Professor of Berkeley Michael (B.) Jordan, American Actor The name ambiguity has raised serious prob- lems in many relevant areas, such as web person search, data integration, link analysis and know- ledge base population. For example, in response to a person query, search engine returns a long, flat list of results containing web pages about several namesakes. The users are then forced either to refine their query by adding terms, or to browse through the search results to find the per- son they are seeking. Besides, an ever-increasing number of question answering and information extraction systems are coming to rely on data from multi-sources, where name ambiguity will lead to wrong answers and poor results. For ex- ample, in order to extract the birth date of the Berkeley professor Michael Jordan, a system may return the birth date of his popular name- sakes, e.g., the basketball player Michael Jordan. So there is an urgent demand for efficient, high-quality named entity disambiguation me- thods. Currently, the common methods for named entity disambiguation include name ob- servation clustering (Bagga and Baldwin, 1998) and entity linking with knowledge base (McNa- mee and Dang, 2009). In this paper, we focus on the method of name observation clustering. Giv- en a set of observations O = {o1, o2, …, on} of the target name to be disambiguated, a named entity disambiguation system should group them into a set of clusters C = {c1, c2, …, cm}, with each re- sulting cluster corresponding to one specific enti- ty. For example, consider the following four ob- …

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