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Applying the Multiple Cause Mixture Model to Text Categorization

机译：将多原因混合模型应用于文本分类

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This paper introduces the use of the Multiple Cause Mixture Model to automatic text category assignment. Although much research has been done on text categorization, this algorithm is novel in that is unsupervised, that is, does not require pre-labeled training examples, and it can assign multiple category labels to documents. In this paper we present very preliminary results of the application of this model to a standard test collection, evaluating it in supervised mode in order to facilitate comparison with other methods, and showing initial results of its use in unsupervised mode.

机译：本文介绍了多原因混合模型在自动文本类别分配中的使用。尽管已经对文本分类进行了大量研究，但是该算法是新颖的，因为它是无监督的，即不需要预先标记的训练示例，并且可以为文档分配多个类别标签。在本文中，我们介绍了将该模型应用于标准测试集的非常初步的结果，在监督模式下对其进行了评估，以便于与其他方法进行比较，并显示了在无监督模式下使用该模型的初步结果。

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来源
《Machine learning》|1996年|435-443|共9页
会议地点 Bari(IT);Bari(IT)
作者
Mehran Sahami; Marti Hearst; Eric Saund;
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作者单位

Gates Building 1A Computer Science Department Stanford University Stanford, CA 94305-9010;

Xerox Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, CA 94304;

Xerox Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, CA 94304;

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正文语种 eng
中图分类计算机的应用;
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Applying the Multiple Cause Mixture Model to Text Categorization

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