|Title||Automatic assignment of biomedical categories: toward a generic approach.|
|Publication Type||Journal Article|
|Year of Publication||2006|
|Journal||Bioinformatics (Oxford, England)|
|Date Published||2006 Mar 15|
|Keywords||Abstracting and Indexing as Topic, Algorithms, Artificial Intelligence, Documentation, MEDLINE, Natural Language Processing, Pattern Recognition, Automated, Periodicals as Topic, Proteins|
MOTIVATION: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent.
METHODS: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units.
RESULTS AND CONCLUSION: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.