Automatic document classification of biological literature

Background: Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a textmining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results: We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: David Chen, Hans-Michael Müller, Paul W. Sternberg
• Keywords: Biological Literature, BMCBI 2006, Caenorhabditis Elegans, Documents