Abstract:
A processor implemented method of identifying the genre of a machine readable, untagged text. The processor implemented method begins by generating a cue vector from the text, which represents occurrences in the text of a first set of nonstructural, surface cues, which are easily computable. Afterward, the processor determines whether the text is an instance of a first text genre using the cue vector and a weighting vector associated with the first text genre.
Abstract:
Arbitrarily large document collections are processed by expanding a focus set having at least one initial metadocument (82) into a plurality of subsequent metadocuments (83,84,85,86). The number of subsequent metadocuments is approximately equal to a predetermined maximum number. The subsequent metadocuments are then clustered into a predetermined number of new metadocuments, which are summarized and presented to a user. The focus set is redefined to include only user-selected new metadocuments.
Abstract:
A method of automatically generating feature probabilities that allow later automatic generation of document extracts. The computer system generates the probabilities by analyzing each document a document at a time. First, the computer system designates one of the documents as a selected document. Next, the computer system analyzes each sentence of the selected document to determine the value of the paragraph feature and the value of the uppercase feature. The computer system repeats this effort for each document of the document corpus. Afterward, the number of occurrences of each value of each feature is calculated and is used to calculate feature value probabilities for all of the features.
Abstract:
An information retrieval system and method are provided in which an operator inputs (110) one or more query words which are used to determine a search key (120) for searching (130) through a corpus of documents, and which returns ( 140) any matches between the search key and the corpus of documents as a phrase containing the word data matching the search key (the query word(s)), a non-stop (content) word next adjacent to the matching word data, and all intervening stop-words between the matching word data and the next adjacent non-stop word. The operator, after reviewing one or more of the returned phrases can then use one or more of the next adjacent non-stop-words as new query words to reformulate the search key ( 150, 160, 170) and perform a subsequent search through the document corpus. This process can be conducted iteratively, until the appropriate documents of interest are located. The additional non-stop-words from each phrase are preferably aligned with each other (e.g., by columnation) to ease viewing of the " new" content words.
Abstract:
A processor implemented method of identifying the genre of a machine readable, untagged text. The processor implemented method begins by generating a cue vector from the text, which represents occurrences in the text of a first set of nonstructural, surface cues, which are easily computable. Afterward, the processor determines whether the text is an instance of a first text genre using the cue vector and a weighting vector associated with the first text genre.