Enhancing information retrieval by automatic acquisition of textual relations using genetic programming

Abstract

We have explored a novel method to find textual relations in electronic documents using genetic programming and semantic networks. This can be used for enhancing information retrieval and simplifying user interfaces. The automatic extraction of relations from text enables easier updating of electronic dictionaries and may reduce interface area both for search input and hit output on small screens such as cell phones and PDAs (Personal Digital Assistants).

Evolving Rules for Document Classification

“…For example FOLLOWS X Y [9] indicates that the word matched by N-Grams Y must follow the word matched by N-Gram X in the text of a document. • Kleene's star (*) could be included as a marker for an arbitrary sequence of characters, e.g.…”

“…We then provide information concerning the implementation of our application and the initial results we have obtained on a text classification task. Although GP has been used in a textual environment [8,9] it has not previously been used to evolve compressed classifiers based on evolving N-Gram patterns.…”

Evolving Text Classification Rules With Genetic Programming

“…Although GP has been used in a textual environment (Clack et al 1997;Bergström et al 2000) it has not previously been used to evolve classifiers based on evolving NGram patterns.…”

“…For example FOLLOWS X Y (Bergström 2000) indicates that the word matched by N-Grams Y must follow the word matched by N-Gram X in the text of a document.  Kleene's star (*) could be included as a marker for an arbitrary sequence of characters, e.g.…”

Evolving Lucene search queries for text classification

“…Although GP has been used in a textual environment [4][5] it has not previously been used to evolve search query classifiers for large text datasets.…”