Abstract: Disclosed herein are methods, articles of manufacture, and systems for translating text. Such a method includes generating a conceptual representation space based on a plurality of source-language documents and a plurality of target-language documents. The method also includes generating, in the conceptual representation space, respective representations of a new source-language document and each of a plurality of dictionaries. The method further includes selecting a first dictionary from the plurality of dictionaries responsive to a similarity between the representation of the new source-language document and the representation of the first dictionary. The method still further includes translating, by using the first dictionary, a term in the new source-language document into a target-language term.

Abstract: An embodiment of the present invention provides a method for automatically translating text. First, a conceptual representation space is generated based on source-language documents and target-language documents, wherein respective terms from the source-language and target-language documents have a representation in the conceptual representation space. Second, a new source-language document is represented in the conceptual representation space, wherein a subset of terms in the new source-language document is represented in the conceptual representation space, such that each term in the subset has a representation in the conceptual representation space. Then, a term in the new source-language document is automatically translated into a corresponding target-language term based on a similarity between the representation of the term and the representation of the corresponding target-language term.

Abstract: Extensions to latent semantic indexing (LSI), including: phrase processing, creation of generalized entities, elaboration of entities, replacement of idiomatic expressions, and use of data fusion methods to combine the aforementioned extensions in a synergistic fashion. Additionally, novel methods tailored to specific applications of LSI are disclosed.

Abstract: Refining a current query. Receiving information regarding the relevancy of documents retrieved from a document collection in response to a current query. Ranking the retrieved documents in accordance with the relevancy information. Forming a candidate query based on the rankings and analysis of locations of the retrieved documents in a latent semantic index vector space formed from the retrieved document. Applying the candidate query to the document collection. Ranking the documents retrieved in response to the candidate query in accordance with the received relevancy information. Comparing the ranking of documents retrieved in response to the candidate query and the ranking of documents retrieved in response to the current query with the received relevancy information. Choosing the query that produces the best ranking.

Abstract: Methods and systems for forming and testing hypotheses regarding improving the correspondence between information needs and data retrieved in response to those needs. In a preferred embodiment, user feedback regarding relevancy of retrieved data is used in combination with the technique of latent semantic indexing to independently facilitate both hypothesis formation and testing.

Abstract: Methods and systems for forming and testing hypotheses regarding improving the correspondence between information needs and data retrieved in response to those needs. In a preferred embodiment, user feedback regarding relevancy of retrieved data is used in combination with the technique of latent semantic indexing to independently facilitate both hypothesis formation and testing.

Abstract: Extensions to latent semantic indexing (LSI), including: phrase processing, creation of generalized entities, elaboration of entities, replacement of idiomatic expressions, and use of data fusion methods to combine the aforementioned extensions in a synergistic fashion. Additionally, novel methods tailored to specific applications of LSI are disclosed.