基于SVM融合多特征的介词结构自动识别  

Feature-rich Prepositional Phrase Boundary Identification based on SVM

作　　者：温苗苗[1] 吴云芳[1] 

机构地区：[1]北京大学计算语言学研究所计算语言学教育部重点实验室(北京大学)

出　　处：《中文信息学报》2009年第5期19-24,共6页Journal of Chinese Information Processing

基　　金：国家863高技术研究发展计划基金项目(2007AA01Z198);国家自然科学基金项目(60703063);国家社会科学基金项目(08CYY016);国家973重点基础研究发展规划基金项目(2004CB318102)

摘　　要：介词结构在汉语文本中出现频率很高,正确识别介词结构边界对句法分析、语音合成中的韵律短语划分有着重要意义。该文较为系统地探讨了汉语中常用介词的边界识别问题。利用支持向量机SVM模型,基于输出概率而不是简单的二分法来选择正确的后边界。探讨了不同的特征选择,并尝试加入语义信息等不同特征组合以提高识别准确率。对常用的68个介词进行边界识别实验,5折交叉验证的准确率达到90.95%,优于前人的识别结果。Owing to the high frequency of prepositional structure, this paper systematically explores the prepositional phrase boundary identification, which plays an important role in Chinese parsing, as well as for some application systems such as text to speech system. We apply support vector machine model to identify phrase boundary, and the boundary word is selected based on the output probability rather than the binary classification results. We also in- vestigate different kinds of features, and try to employ rich features such as semantic classes involved. Together 68 frequently used prepositions are experimented in our test and the results show it achieves a precision of 90.95% in a five fold cross validation.

关 键 词：计算机应用 中文信息处理 介词结构识别 支持向量机 语义类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]