Abstract: Methods and systems for generating textual information are disclosed. In one exemplary embodiment, a method of generating textual information is disclosed that comprises identifying a plurality of candidate summaries related to textual information based at least in part on a document, determining first and second attribute values based at least in part on the candidate summaries, and determining an optimal candidate summary based at least in part on the first and second attribute values.

Abstract: The method of the present invention utilizes machine-learning techniques, particularly Support Vector Machines in combination with a neural network, to process a unique machine-learning enabled representation of the audio bitstream. Using this method, a classifying machine is able to autonomously detect characteristics of a piece of music, such as the artist or genre, and classify it accordingly. The method includes transforming digital time-domain representation of music into a frequency-domain representation, then dividing that frequency data into time slices, and compressing it into frequency bands to form multiple learning representations of each song. The learning representations that result are processed by a group of Support Vector Machines, then by a neural network, both previously trained to distinguish among a given set of characteristics, to determine the classification.

Abstract: Systems and methods for constructing a query result set are described. In one described method, a display processor or query system refreshes a first result set comprising a first plurality of article identifiers. The display processor or query system receives or creates a second result set comprising a second plurality of article identifiers. The display processor then creates a third result set based at least in part on the first result set and the second result set. The display processor may create the third result set by modifying the first result set based at least in part on the second plurality of article identifiers or by modifying the second result set based at least in part on the first plurality of article identifiers.

Abstract: Systems and methods for associating a keyword with a window are described. In one described system, a computer program, such as an indexer, captures processor, or query system, associates a keyword with a first user interface area. The query system or other computer program receives a signal that the first user interface is inactive and that a second user interface area is active and, in response, generates an implicit search query that includes the keyword associated with the first user interface area.

Abstract: The invention is a method for retrieving answers to questions from an information retrieval system. The method involves automatically learning phrase features for classifying questions into different types, automatically generating candidate query transformations from a training set of question/answer pairs, and automatically evaluating the candidate transforms on information retrieval systems. At run time, questions are transformed into a set of queries, and re-ranking is performed on the documents retrieved.

Abstract: A method automatically determines groups of words or phrases that are descriptive names of a small set of documents, as well as infers concepts in the small set of documents that are more general and more specific than the descriptive names, without any prior knowledge of the hierarchy or the concepts, in a language independent manner. The descriptive names and the concepts may not even be explicitly contained in the documents. The primary application of the invention is for searching of the World Wide Web, but the invention is not limited solely to use with the World Wide Web and may be applied to any set of documents. Classes of features are identified in order to promote understanding of a set of documents. Preferably, there are three classes of features. “Self” features or terms describe the cluster as a whole. “Parent” features or terms describe more general concepts. “Child” features or terms describe specializations of the cluster.

Abstract: A computer implemented meta search engine and search method. In accordance with this method, a query is forwarded to one or more third party search engines, and the responses from the third party search engine or engines are parsed in order to extract information regarding the documents matching the query. The full text of the documents matching the query are downloaded, and the query terms in the documents are located. The text surrounding the query terms are extracted, and that text is displayed.

Abstract: An autonomous citation indexing system which can be used as an assistant agent automates and enhances the task of finding publications in electronic form, including publications located on the world wide web. The system parses citations from papers and identifies citations to the same paper that may differ in syntax. The system also extracts and provides the context of citations to a given paper, allowing a researcher to determine what is published in other papers about a given paper. Common citations and word or string vector distance similarity are used to find related articles in a search.

Abstract: An enhanced method and system for the classification of a target web page and the description of a set of web pages web pages utilizing virtual documents, in which a virtual document comprises extended anchortext extracted from each of a plurality of web pages that includes at least one hyperlink citing each target web page.

Abstract: A method automatically determines groups of words or phrases that are descriptive names of a small set of documents, as well as infers concepts in the small set of documents that are more general and more specific than the descriptive names, without any prior knowledge of the hierarchy or the concepts, in a language independent manner. The descriptive names and the concepts may not even be explicitly contained in the documents. The primary application of the invention is for searching of the World Wide Web, but the invention is not limited solely to use with the World Wide Web and may be applied to any set of documents. Classes of features are identified in order to promote understanding of a set of documents. Preferably, there are three classes of features. “Self” features or terms describe the cluster as a whole. “Parent” features or terms describe more general concepts. “Child” features or terms describe specializations of the cluster.

Abstract: The method of the present invention utilizes machine-learning techniques, particularly Support Vector Machines in combination with a neural network, to process a unique machine-learning enabled representation of the audio bitstream. Using this method, a classifying machine is able to autonomously detect characteristics of a piece of music, such as the artist or genre, and classify it accordingly. The method includes transforming digital time-domain representation of music into a frequency-domain representation, then dividing that frequency data into time slices, and compressing it into frequency bands to form multiple learning representations of each song. The learning representations that result are processed by a group of Support Vector Machines, then by a neural network, both previously trained to distinguish among a given set of characteristics, to determine the classification.

Abstract: The invention is a method for retrieving answers to questions from an information retrieval system. The method involves automatically learning phrase features for classifying questions into different types, automatically generating candidate query transformations from a training set of question/answer pairs, and automatically evaluating the candidate transforms on information retrieval systems. At run time, questions are transformed into a set of queries, and re-ranking is performed on the documents retrieved.

Abstract: A selective retrieval metasearch engine uses relevance estimation and confidence computation to select documents for which additional information is to be obtained. The additional information is used to update relevance estimation for the selected documents. A selective retrieval metasearch engine improves execution time, resource usage, throughput, and/or result quality.

Abstract: An autonomous citation indexing system which can be used as an assistant agent automates and enhances the task of finding publications in electronic form, including publications located on the world wide web. The system parses citations from papers and identifies citations to the same paper that may differ in syntax. The system also extracts and provides the context of citations to a given paper, allowing a researcher to determine what is published in other papers about a given paper. Common citations and word or string vector distance similarity are used to find related articles in a search.

Abstract: An autonomous citation indexing system which can be used as an assistant agent automates and enhances the task of finding publications in electronic form, including publications located on the world wide web. The system parses citations from papers and identifies citations to the same paper that may differ in syntax. The system also extracts and provides the context of citations to a given paper, allowing a researcher to determine what is published in other papers about a given paper. Common citations and word or string vector distance similarity are used to find related articles in a search.