T-HMM: A Novel Biomedical Text Classifier Based on Hidden Markov Models

被引：8

作者：

Seara Vieira, A. ^{[1
]}

Iglesias, E. L. ^{[1
]}

Borrajo, L. ^{[1
]}

机构：

[1] Univ Vigo, Escola Super Enxeneria Informat, Dept Comp Sci, Orense, Spain

来源：

8TH INTERNATIONAL CONFERENCE ON PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS (PACBB 2014) | 2014年 / 294卷

关键词：

Hidden Markov Model; Text classification; Bioinformatics; DOCUMENTS;

D O I：

10.1007/978-3-319-07581-5_27

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：225 / 234

页数：10

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