基于SVM(support vector machine)理论的分类算法,由于其完善的理论基础和良好的实验结果,目前已逐渐引起国内外研究者的关注。和其他分类算法相比,基于结构风险最小化原则的SVM在小样本模式识别中表现较好的泛化能力。文本组块分析作为句法分析的预处理阶段,通过将文本划分成一组互不重叠的片断,来达到降低句法分析的难度。本文将中文组块识别问题看成分类问题,并利用SVM加以解决。实验结果证明,SVM算法在汉语组块识别方面是有效的,在哈尔滨工业大学树库语料测试的结果是F=88.67%,并且特别适用于有限的汉语带标信息的情况。
Abstract
The classification algorithm based on SVM (support vector machine) attracts more attention from researchers due to its perfect theoretical properties and good empirical results. Compared with other classification algorithms , structural risk minimizations based SVM achieve high generalization performance with small number of samples. The text chunking , as a preprocessing step for parsing , is to divide text into syntactically related non-overlapping groups of words (chunks) , reducing the complexity of the full parsing. In this paper , we treat Chinese text chunking as a classification problem , and apply SVM to solve it . The chunking experiments were carried out on the HIT Chinese Treebank corpus. Experimental results show that it is an effective approach , achieving an F score of 88.67% , especially for a small number of Chinese labeled samples.
关键词
计算机应用 /
中文信息处理 /
支持向量机 /
结构风险最小化 /
文本组块
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Key words
computer application /
Chinese information processing /
support vector machine /
structural risk minimization /
text chunking
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参考文献
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脚注
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基金
国家自然科学基金资助项目(60083006);国家重点基础研究发展规划973资助项目(G19980305011);国家自然科学基金和微软亚洲研究院联合资助项目(60203019)
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