Nec Laboratories America, Inc.

Nec Research Institute, Inc.

Stephen R Lawrence

C Lee Giles

Kurt D Bollacker

Brian Whitman

David Myer Pennock

Yevgeny E Agichtein

Gary William Flake

Eric J Glover

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.

Extracting classifying data in music from an audio bitstream

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.

Method for retrieving answers from an information retrieval system

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. &#x201c;Child&#x201d; features or terms describe specializations of the cluster. The self features can be used as a recommended name for a cluster, while parents and children can be used to place the clusters in the space of a larger collection. Parent features suggest a more general concept, while children features suggest concepts that describe a specialization of the self feature(s). Automatic discovery of parent, self and child features is useful for several purposes including automatic labeling of web directories and improving information retrieval.

Inferring hierarchical descriptions of a set of documents

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.

Meta search engine

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.

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New York, NY, United States of America

Stephen R Lawrence