Extracting opinion sentence by combination of SVM and syntactic templates

Bo Zhang, Yanquan Zhou, Yu Mao
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引用次数: 5

Abstract

This paper presents a combined method of syntactic structure, dependency relation and SVM classifier to extract opinion sentences. At first, we use the syntactic structure templates with high confidence summarized artificially and the dependency relation templates with high precision obtained by a dependency relation extraction algorithm to tag sentences as opinion sentence. Then we input the remaining test data to a trained SVM classifier which is created by a rigorous process of feature selection. Eventually the combined method performed well, achieving 92.6% recall with 85.5% precision.
基于支持向量机和句法模板的观点句提取方法
提出了一种结合句法结构、依赖关系和支持向量机分类器的观点句提取方法。首先,我们使用人工总结的高置信度句法结构模板和依赖关系提取算法获得的高精度依赖关系模板将句子标记为意见句。然后将剩余的测试数据输入到经过训练的SVM分类器中,该分类器通过严格的特征选择过程生成。最终,联合方法取得了良好的结果,召回率为92.6%,准确率为85.5%。
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