采用支持向量机(SVM)方法实现搜索引擎日志中“N+V+N”、“V+N+N”型短语功能类别识别.通过选取不同特征,构建多特征模板,实现对“N+V+N”、“V+N+N”型短语中名词短语、动词短语、主谓短语三种功能短语的自动识别,并且针对不同词性标注集对实验结果是否有影响进行了实验.实验结果显示,SVM在搜索引擎日志短语识别中有很高的识别率.%This paper proposes to use Support Vector Machine (SVM) model to recognize the function category of "N + V + N" and " V + N + N" structure phrase in search engine logs. By selecting different features, constructs the multi-feature templates to automatically recognize noun phrases, verb phrases and subject-predicate phrases three kinds of function phrases in the structure of "N + V + N" and " V + N + N" and has carried on the experiment to test the different part-of-speech tagging to experimental result' s influence. The experimental result showed that SVM has the very high recognition rate in the search engine diary phrase recognition.
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