Battelle Memorial Institute

Stuart J Rose

Wendy E Cowley

Vernon L Crow

Nicholas O Cramer

Systems and computer-implemented processes for identification of features and determination of feature associations in a group of documents can involve providing a plurality of keywords identified among the terms of at least some of the documents. A value measure can be calculated for each keyword. High-value keywords are defined as those keywords having value measures that exceed a threshold. For each high-value keyword, term-document associations (TDA) are accessed. The TDA characterize measures of association between each term and at least some documents in the group. A processor quantifies similarities between unique pairs of high-value keywords based on the TDA for each respective high-value keyword and generates a similarity matrix that indicates one or more sets that each comprise highly associated high-value keywords.

Systems and processes for identifying features and determining feature associations in groups of documents

Methods and systems for automatically generating lists of stop words for information retrieval and analysis. Generation of the stop words can include providing a corpus of documents and a plurality of keywords. From the corpus of documents, a term list of all terms is constructed and both a keyword adjacency frequency and a keyword frequency are determined. If a ratio of the keyword adjacency frequency to the keyword frequency for a particular term on the term list is less than a predetermined value, then that term is excluded from the term list. The resulting term list is truncated based on predetermined criteria to form a stop word list.

Automatic generation of stop word lists for information retrieval and analysis

Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

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Richland, WA, United States of America

Stuart J Rose