Raytheon Bbn Technologies Corp.

Ramp Holdings, Inc.

Elizabeth Megan Boschee

Marjorie Ruth Freedman

Michael Levit

Ralph Mark Weischedel

Alex Baron

The invention relates to cross-document entity co-reference systems in which naturally occurring entity mentions in a document corpus are analyzed and transformed into name clusters that represent global entities. In a first aspect of the invention, a name variation module analyzes naturally occurring names of entities extracted from the document corpus and provides an initial set of equivalent names that could refer to the same real world entity. In a second aspect of the invention, a disambiguation module takes the initial set of equivalent names and uses an agglomerative clustering algorithm to disambiguate the potentially co-referent named entities.

Confidence links between name entities in disparate documents

The invention relates to topic classification systems in which text intervals are represented as proposition trees. Free-text queries and candidate responses are transformed into proposition trees, and a particular candidate response can be matched to a free-text query by transforming the proposition trees of the free-text query into the proposition trees of the candidate responses. Because proposition trees are able to capture semantic information of text intervals, the topic classification system accounts for the relative importance of topic words, for paraphrases and re-wordings, and for omissions and additions. Redundancy of two text intervals can also be identified.

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Watertown, MA, United States of America

Elizabeth Megan Boschee