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 text-mining 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), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept.


CONCLUSIONS
We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept.
Chen
David
D
Division of Biology and Howard Hughes Medical Institute, California Institute of Technology, Pasadena, California, USA. davidc@caltech.edu
Müller
Hans-Michael
HM
Sternberg
Paul W
PW
D000043:Abstracting and Indexing / Q000379:methods; D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000166:cytology / Q000235:genetics / Q000502:physiology; D016000:Cluster Analysis; D016206:Databases, Bibliographic / Q000145:classification; D016247:Information Storage and Retrieval / Q000379:methods*; D011642:Publications / Q000145:classification*; D014584:User-Computer Interface