A HMM text classification model with learning capacity

• IGLESIAS, Eva L. ^[1] ; BORRAJO, Lourdes ^[1] ; ROMERO, R. ^[1] ; A. Seara Vieira
  1. [1] Universidade de Vigo

     Universidade de Vigo

     Vigo, España

     
• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 3, Nº. 3, 2014, págs. 21-34
• Idioma: inglés
• DOI: 10.14201/ADCAIJ2014332134
• Enlaces
  • Texto completo
• Resumen

  • In this paper a method of classifying biomedical text documents based on Hidden Markov Model is proposed and evaluated. The method is integrated into a framework named BioClass. Bioclass is composed of intelligent text classification tools and facilitates the comparison between them because it has several views of the results. The main goal is to propose a more effective based-on content classifier than current methods in this environment To test the effectiveness of the classifier presented, a set of experiments performed on the OSHUMED corpus are preseted. Our model is tested adding it learning capacity and without it, and it is compared with other classification techniques. The results suggest that the adaptive HMM model is indeed more suitable for document classification.

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