Robust Reading: Identification and Tracing of Ambiguous Names

Robust Reading: Identification and Tracing of Ambiguous Names Xin Li Paul Morie Dan Roth Department of Computer Science University of Illinois, Urbana, IL 61801 {xli1,morie,danr}@uiuc.edu Abstract A given entity, representing a person, a location or an organization, may be mentioned in text in multiple, ambiguous ways. Understanding natural language requires identifying whether different mentions of a name, within and across documents, represent the same entity. We develop an unsupervised learning approach that is shown to resolve accurately the name identification and tracing problem. At the heart of our approach is a generative model of how documents are generated and how names are “sprinkled” into them. In its most general form, our model assumes: (1) a joint distribution over entities, (2) an “author” model, that assumes that at least one mention of an entity in a docu- ment is easily identifiable, and then generates other mentions via (3) an appearance model, governing how mentions are transformed from the “representative” mention. We show how to estimate the model and do inference with it and how this resolves several aspects of the prob- lem from the perspective of applications such as questions answering. 1 Introduction Reading and understanding text is a task that requires the ability to disambiguate at several levels, abstracting away details and using background knowledge in a variety of ways. One of the difficulties that humans resolve instan- taneously and unconsciously is that of reading names. Most names of people, locations, organizations and oth- ers, have multiple writings that are used freely within and across documents. The variability in writing a given concept, along with the fact that different concepts may have very similar writings, poses a significant challenge to progress in nat- ural language processing. Consider, for example, an open domain question answering system (Voorhees, 2002) that attempts, given a question like: “When was President Kennedy born?” to search a large collection of articles in order to pinpoint the concise answer: “on May 29, 1917.” The sentence, and even the document that contains the answer, may not contain the name “President Kennedy”; it may refer to this entity as “Kennedy”, “JFK” or “John Fitzgerald Kennedy”. Other documents may state that “John F. Kennedy, Jr. was born on November 25, 1960”, but this fact refers to our target entity’s son. Other men- tions, such as “Senator Kennedy” or “Mrs. Kennedy” are even “closer” to the writing of the target entity, but clearly refer to different entities. Even the statement “John Kennedy, born 5-29-1941” turns out to refer to a different entity, as one can tell observing that the doc- ument discusses Kennedy’s batting statistics. A similar problem exists for other entity types, such as locations, organizations etc. Ad hoc solutions to this problem, as we show, fail to provide a reliable and accurate solution. This paper presents the first attempt to apply a unified approach to all major aspects of this problem, presented here from the perspective of the question answering task: (1) Entity Identity - do mentions A and …

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