Ip.com I, LLC

James Thomas Durkin

Wen Ruan

Samuel C Baxter

William Yurich Fowlkes

Natalia Victorovna Britvikhina

Ju Suk Lee

Robert Solmer

The present teaching relates to method, system, medium, and implementations for modeling an entity based on textual information. When information about an entity to be modeled is received, textual information related to the entity is searched, including a plurality of documents related to at least one aspect of the entity. First aggregated semantic models are obtained for the plurality of documents. Each first aggregated semantic model represents one of the plurality of documents and includes a semantic feature vector and a semantic signature. Such first aggregated semantic models are clustered into at least one group, each of which corresponds to one of the at least one aspect of the entity and includes a set of first aggregated semantic models representing semantics of documents related to the aspect of the entity. A second aggregated semantic model is then derived based on each group of first aggregated semantic models to characterize one aspect of the entity, yielding one or more second aggregated semantic models. A second aggregated semantic model for an aspect of the entity comprises an aggregated semantic feature vector and an aggregated semantic signature for characterizing the aspect of the entity and facilitating a search with respect to the aspect.

System and method for aggregated modeling, search, visualization, and summarization and applications thereof

The present teaching relates to method and system for assessing semantic distance. A query is received and processed, by a document representation constructor, to obtain a first query representation. The query is then processed by a query representation constructor to obtain a second query representation. Based on the first and second query representations, a dynamic norm is determined. Based on the second query representation, a plurality of documents are identified as being related to the query and a semantic distance between the query and the plurality of documents is determined based on the dynamic norm.

System and method for dynamically normalized semantic distance and applications thereof

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Pittsford, NY, United States of America

James Thomas Durkin