Perilog measures the degree of contextual association of large numbers of term pairs in text to produce network models that capture the structure of the text and, by virtue of Perilog’s validated theory of iconicity, the structure of the domains, situations, and concerns expressed by the author of the text. Given alphanumeric representations of any other sequences in which context is meaningful — such as music or generic sequences — Perilog can derive their contextual structure.
Perilog operates on a document set or a single document, creating a network model of contextually related words and phrases. When a user enters a keyword or key phrase search, Perilog creates a query network of “topical hubs” based on the query words input by the user. Phrases may be of any number and length. A network represents each phrase. Such networks are combined into a single query network.
By matching the phrase query network with document networks, Perilog’s phrase search provides flexible and thorough phrase matching that is unavailable with other methods. Instead of the keyword search being limited to the query words alone, Perilog uses the relationships of keywords within their contextual associations to find documents in which those relationships are significant.
Perilog’s key features and methods encompass text analysis, modeling, relevance ranking, keyword and phrase search, phrase generation, and phrase recovery.
This work was done by Michael McGreevy of Ames Research Center.