An Entity-Topic Model for Entity Linking

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 105–115, Jeju Island, Korea, 12–14 July 2012. c⃝2012 Association for Computational Linguistics An Entity-Topic Model for Entity Linking Xianpei Han Le Sun Institute of Software, Chinese Academy of Sciences HaiDian District, Beijing, China. {xianpei, sunle}@nfs.iscas.ac.cn Abstract Entity Linking (EL) has received considerable attention in recent years. Given many name mentions in a document, the goal of EL is to predict their referent entities in a knowledge base. Traditionally, there have been two distinct directions of EL research: one focusing on the effects of mention’s context compatibility, assuming that “the referent entity of a mention is reflected by its context”; the other dealing with the effects of document’s topic coherence, assuming that “a mention’s referent entity should be coherent with the document’s main topics”. In this paper, we propose a generative model – called entity- topic model, to effectively join the above two complementary directions together. By jointly modeling and exploiting the context compatibility, the topic coherence and the correlation between them, our model can accurately link all mentions in a document using both the local information (including the words and the mentions in a document) and the global knowledge (including the topic knowledge, the entity context knowledge and the entity name knowledge). Experimental results demonstrate the effectiveness of the proposed model. 1 Introduction Entity Linking (EL) has received considerable research attention in recent years (McNamee & Dang, 2009; Ji et al., 2010). Given many name mentions in a document, the goal of EL is to predict their referent entities in a given knowledge base (KB), such as the Wikipedia1. For example, as 1 www.wikipedia.org shown in Figure 1, an EL system should identify the referent entities of the three mentions WWDC, Apple and Lion correspondingly are the entities Apple Worldwide Developers Conference, Apple Inc. and Mac OS X Lion in KB. The EL problem appears in many different guises throughout the areas of natural language processing, information retrieval and text mining. For instance, in many applications we need to collect all appearances of a specific entity in different documents, EL is an effective way to resolve such an information integration problem. Furthermore, EL can bridge the mentions in documents with the semantic information in knowledge bases (e.g., Wikipedia and Freebase 2 ), thus can provide a solid foundation for knowledge-rich methods. Figure 1. A Demo of Entity Linking Unfortunately, the accurate EL is often hindered by the name ambiguity problem, i.e., a name may refer to different entities in different contexts. For example, the name Apple may refer to more than 20 entities in Wikipedia, such as Apple Inc., Apple (band) and Apple Bank. Traditionally, there have been two distinct directions in EL to resolve the name ambiguity problem: one focusing on the effects of mention’s context compatibility and the other dealing with the effects of document’s topic coherence. EL methods based on context 2 www.freebase.com At the WWDC conference, Apple …

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