A Generative Entity-Mention Model for Linking Entities with Knowledge Base

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 945–954, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics A Generative Entity-Mention Model for Linking Entities with Knowledge Base Xianpei Han Le Sun Institute of Software, Chinese Academy of Sciences HaiDian District, Beijing, China. {xianpei, sunle}@nfs.iscas.ac.cn Abstract Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entity- mention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods. 1 Introduction In recent years, due to the proliferation of knowledge-sharing communities like Wikipedia 1 and the many research efforts for the automated knowledge base population from Web like the Read the Web2 project, more and more large-scale knowledge bases are available. These knowledge bases contain rich knowledge about the world’s entities, their semantic properties, and the semantic relations between each other. One of the most notorious examples is Wikipedia: its 2010 English 1 http://www.wikipedia.org/ 2 http://rtw.ml.cmu.edu/ version contains more than 3 million entities and 20 million semantic relations. Bridging these knowledge bases with the textual data can facilitate many different tasks such as entity search, information extraction and text classification. For example, as shown in Figure 1, knowing the word Jordan in the document refers to a basketball player and the word Bulls refers to a NBA team would be helpful in classifying this document into the Sport/Basketball class. After a standout career at the University, joined the in 1984. Michael Jeffrey Jordan NBA Player Basketball Player Chicago Bulls NBA Sport Organization NBA Team Knowledge Base Employer-of IS-A IS-A IS-A IS-A IS-A Part-of Jordan Bulls Figure 1. A Demo of Entity Linking A key issue in bridging the knowledge base with the textual data is linking the entities in a document with their referents in a knowledge base, which is usually referred to as the Entity Linking task. Given a set of name mentions M = {m1, m2, …, mk} contained in documents and a knowledge base KB containing a set of entities E = {e1, e2, …, en}, an entity linking system is a function : M E   which links these name mentions to their referent entities …

Content truncated to 500 words