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 document 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 problem from the perspective of applications such as questions answering.
REF solved the robust reading problem by adopting a probabilistic view on how documents are generated and how names are sprinkled into them.
Robust Reading: Identification And Tracing Of Ambiguous Names
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science