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| 基于机器学习的文本分类技术研究进展 |
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苏金树1, 张博锋1, 徐昕1,2
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1.国防科学技术大学,计算机学院,湖南,长沙,410073;2.国防科学技术大学,机电工程与自动化学院,湖南,长沙,410073
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| 摘要: |
| 文本自动分类是信息检索与数据挖掘领域的研究热点与核心技术,近年来得到了广泛的关注和快速的发展.提出了基于机器学习的文本分类技术所面临的互联网内容信息处理等复杂应用的挑战,从模型、算法和评测等方面对其研究进展进行综述评论.认为非线性、数据集偏斜、标注瓶颈、多层分类、算法的扩展性及Web页分类等问题是目前文本分类研究的关键问题,并讨论了这些问题可能采取的方法.最后对研究的方向进行了展望. |
| 关键词: 自动文本分类 机器学习 降维 核方法 未标注集 偏斜数据集 分级分类 大规模文本分类 Web页分类 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.90604006, 60303012 (国家自然科学基金); the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20049998027 (国家教育部高校博士点基金) |
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| Advances in Machine Learning Based Text Categorization |
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SU Jin-Shu,ZHANG Bo-Feng,XU Xin
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| Abstract: |
| In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given. |
| Key words: automatic text categorization machine learning dimensionality reduction kernel method unlabeled data set skewed data set hierarchical categorization large-scale text categorization Web page categorization |
