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A hidden Markov model- based text classification of medical documents

机译:基于隐马尔可夫模型的医学文献文本分类

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摘要

The purpose of the study is to test the application of the hidden Markov model (HMM) using prior knowledge in medical text classification (TC). HMM has been applied to a wide range of applications in information processing, but not so much in TC applications. The Medical Subject Heading (MeSH) is utilized for prior knowledge in the model. A prototype for an HMM-based TC model is designed, and an experimental model based on the prototype is implemented so as to categorize medical documents into MeSH. A subset of OHSUMED is used for the experiments. Our results show that the performance of our model is comparable to those reported in the literature.
机译:该研究的目的是使用先验知识在医学文本分类(TC)中测试隐马尔可夫模型(HMM)的应用。 HMM在信息处理中已被广泛应用,但在TC应用中却没有那么多。医学主题词(MeSH)用于模型中的先验知识。设计了基于HMM的TC模型的原型,并实现了基于该原型的实验模型,以将医学文献分类为MeSH。 OHSUMED的子集用于实验。我们的结果表明,我们模型的性能与文献报道的性能相当。

著录项

  • 来源
    《Journal of Information Science》 |2009年第1期|67-81|共15页
  • 作者

    Kwan Yi; Jamshid Beheshti;

  • 作者单位

    331 Little Fine Arts Library, School of Library and Information Science, University of Kentucky, Lexington, KY 40506, USA;

    School of Information Studies, McGill University, Montreal, Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    hidden Markov model; HMM; MeSH; text classification; UMLS;

    机译:隐马尔可夫模型HMM;啮合;文字分类UMLS;

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