Robust Reading of Ambiguous Writing
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 several aspects of the name identity 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 particular, we model appearance similarity between names representing the same entity, contextual correlation among entities, and co-occurrence probabilities of entities within a document. We show how to estimate the model and do inference with it and how this resolves several aspects of the problem from the perspective of applications such as questions answering.