中文复杂名词短语依存句法分析

被引:3
作者
陈永波
汤昂昂
姬东鸿
机构
[1] 武汉大学计算机学院
基金
国家自然科学基金重点项目;
关键词
中文复杂名词短语; 依存句法分析; 决策式算法; 支持向量机; 特征;
D O I
暂无
中图分类号
TP391.1 [文字信息处理];
学科分类号
081203 ; 0835 ;
摘要
针对中文复杂名词短语的依存句法分析进行了研究,提出简单边优先与SVM相结合的依存句法分析算法。算法的每一步迭代根据边的特征于每一对相邻子树之间的无向边中选择最优者,然后利用支持向量机根据边两端子树的特征确定该边的方向,即得到两棵子树的中心语之间的依存关系。实验证明对于复杂名词短语的依存句法分析,算法准确率比简单边优先算法有明显提高,且优于基于最大生成树算法的中文句法分析器;算法分析效率更高,时间复杂度为O(n2logn)。
引用
收藏
页码:1617 / 1620
页数:4
相关论文
共 16 条
[1]  
机器翻译研究[M]. 中国对外翻译出版公司 , 冯志伟著, 2004
[2]  
Discriminative training methods for hidden Markov models:theory and experiments with perceptron algorithms. Collins M. Proc of Conference on Empirical Methods in Natural Language Processing . 2007
[3]  
The bracketing guidelines for the penn Chinese treebank 3.0. Xue Nianwen,Xia Fei. IRCS-00-08 . 2010
[4]  
An efficient algorithm for easy-first non-directional dependency parsing. Goldberg Y,Elhadad M. Proc of Annual Conference on Human Language Technologies and the North American Chapter of the Association for Computational Linguistics . 2010
[5]  
"Non- Projective Dependency Parsing Using Spanning Tree Algorithms". R. McDonald,F. Pereira,K. Ribarov,J. Haji. HLT ’’05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing . 2005
[6]  
Transition-based parsing of the Chinese treebank using a global discriminative model. Zhang,Yue,Stephen Clark. Proceedings of the 11th International Conference on Parsing Technologies (IWPT 09) . 2009
[7]  
A tale of two parsers: investigating and combining graph-basedand transition-based dependency parsing using beam-search. Zhang Y,Clark S. Proceedings of theConference on Empirical Methods in Natural Language Processing . 2008
[8]  
Online large-margin training of dependency parsers. McDonald R,Crammer K,Pereira F. Proc of the43rd Annual Meeting of the Association for Computational Linguistics . 2005
[9]   Classification of semantic relations between nominals [J].
Girju, Roxana ;
Nakov, Preslav ;
Nastase, Vivi ;
Szpakowicz, Stan ;
Turney, Peter ;
Yuret, Deniz .
LANGUAGE RESOURCES AND EVALUATION, 2009, 43 (02) :105-121
[10]   汉语最长名词短语的自动识别 [J].
周强 ;
孙茂松 ;
黄昌宁 .
软件学报, 2000, (02) :195-201