Large-Scale Named Entity Disambiguation Based on Wikipedia Data

Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 708–716, Prague, June 2007. c⃝2007 Association for Computational Linguistics Large-Scale Named Entity Disambiguation Based on Wikipedia Data Silviu Cucerzan Microsoft Research One Microsoft Way, Redmond, WA 98052, USA silviu@microsoft.com Abstract This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disam- biguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented sys- tem shows high disambiguation accuracy on both news stories and Wikipedia articles. 1 Introduction and Related Work The ability to identify the named entities (such as people and locations) has been established as an important task in several areas, including topic de- tection and tracking, machine translation, and in- formation retrieval. Its goal is the identification of mentions of entities in text (also referred to as sur- face forms henceforth), and their labeling with one of several entity type labels. Note that an entity (such as George W. Bush, the current president of the U.S.) can be referred to by multiple surface forms (e.g., “George Bush” and “Bush”) and a sur- face form (e.g., “Bush”) can refer to multiple enti- ties (e.g., two U.S. presidents, the football player Reggie Bush, and the rock band called Bush). When it was introduced, in the 6th Message Un- derstanding Conference (Grishman and Sundheim, 1996), the named entity recognition task comprised three entity identification and labeling subtasks: ENAMEX (proper names and acronyms designat- ing persons, locations, and organizations), TIMEX (absolute temporal terms) and NUMEX (numeric expressions, monetary expressions, and percent- ages). Since 1995, other similar named entity rec- ognition tasks have been defined, among which CoNLL (e.g., Tjong Kim Sang and De Meulder, 2003) and ACE (Doddington et al., 2004). In addi- tion to structural disambiguation (e.g., does “the Alliance for Democracy in Mali” mention one, two, or three entities?) and entity labeling (e.g., does “Washington went ahead” mention a person, a place, or an organization?), MUC and ACE also included a within document coreference task, of grouping all the mentions of an entity in a docu- ment together (Hirschman and Chinchor, 1997). When breaking the document boundary and scal- ing entity tracking to a large document collection or the Web, resolving semantic ambiguity becomes of central importance, as many surface forms turn out to be ambiguous. For example, the surface form “Texas” is used to refer to more than twenty different named entities in Wikipedia. In the con- text “former Texas quarterback James Street”, Texas refers to the University of Texas at Austin; in the context “in 2000, Texas released a greatest hits album”, Texas refers to the British pop band; in the …

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