T-HMM: A Novel Biomedical Text Classifier Based on Hidden Markov Models
Advances in intelligent systems and computingPublished 1 January 2014
Adrián Seara Vieira, Eva Iglesias, L. Borrajo
Citations9
SNIP0.30
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TL;DR
An original model for the classification of biomedical texts stored in large document corpora is proposed using information retrieval techniques and Hidden Markov Models.
Abstract
In this paper, we propose an original model for the classification of biomedical texts stored in large document corpora. The model classifies scientific documents according to their content using information retrieval techniques and Hidden Markov Models. To demonstrate the efficiency of the model, we present a set of experiments which have been performed on OHSUMED biomedical corpus, a subset of the MEDLINE database, and the Allele and GO TREC corpora. Our classifier is also compared with Naive Bayes, k-NN and SVM techniques. Experiments illustrate the effectiveness of the proposed approach. Results show that the model is comparable to the SVM technique in the classification of biomedical texts.
Keywords
Computer Science
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