TEXTQUEST: DOCUMENT CLUSTERING OF MEDLINE ABSTRACTS FOR CONCEPT DISCOVERY IN MOLECULAR BIOLOGY

1 December 2000

proceedings article
Published by World Scientific Pub Co Pte Ltd in Pacific Symposium on Biocomputing

p. 384-395
https://doi.org/10.1142/9789814447362_0038

Abstract

We present an algorithm for large-scale document clustering of biological text, obtained from Medline abstracts. The algorithm is based on statistical treatment of terms, stemming, the idea of a “go-list”, unsupervised machine learning and graph layout optimization. The method is flexible and robust, controlled by a small number of parameter values. Experiments show that the resulting document clusters are meaningful as assessed by cluster-specific terms. Despite the statistical nature of the approach, with minimal semantic analysis, the terms provide a shallow description of the document corpus and support concept discovery.

Keywords

DOCUMENT CLUSTERING
MEDLINE ABSTRACTS
CONCEPT DISCOVERY
OPTIMIZATION
TREATMENT
STATISTICAL

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