基于SVM的汉语句子片段划分

针对长句子引起句法分析性能下降的问题,本文提出了一种基于SVM的句子片段划分方法:先根据语法结构将句子划分为多个片段,识别出每个片段的类别;然后根据片段的类别将句子分割为几个部分,每个部分作为句法分析的基本单元;最后将句法分析之后的各个部分进行合并,形成完整的分析结果.该方法减小了句法分析的复杂度,提高了分析的准确率.

Chinese Sentence Segmentation based on SVM method

The sentence length has a great influence on the parsing. The complexity of searching algorithm and the amount of ambiguous structures will grow rapidly with the increase of the sentence length. A method of identifying the segments based on the SVM classifier is presented to solve the problem of sentence length in the parsing. Firstly, a sentence is divided into different segments, each of which is assigned a label to indicate its syntactic type. Then the sentence is parsed based on the segments. Finally, all the segments are linked through the dependency relations and the parsing of the whole dependency tree is completed. Experiments show that the identification of segments decreases the complexity of parsing and improves the accuracy of Chinese dependency parsing.