中心词驱动的汉语统计句法分析模型的改进  

Improvements on Head-Driven Probabilistic Parsing for Chinese

作　　者：何亮[1] 戴新宇[1] 周俊生[2] 陈家骏[1] 

机构地区：[1]南京大学计算机软件新技术国家重点实验室,江苏南京210093 [2]南京师范大学计算机科学系,江苏南京210097

出　　处：《中文信息学报》2008年第4期3-9,共7页Journal of Chinese Information Processing

基　　金：国家863高科技计划资助项目(2006AA01Z143,2006AA01Z139);国家自然科学基金资助项目(60673043);国家社科基金资助项目(07BYY051)

摘　　要：在对Dan Bikel基于Collins中心词驱动概率句法分析模型实现的句法分析器进行深入研究分析的基础上,对其进行了两个方面的改进。一是通过提供N-best词性候选序列,改进原模型在词性方面的处理,改善了句法分析的结果;二是在该模型中引进单独的基本名词短语识别,从而降低句法分析的复杂度,提高了效率,其中,针对中文的特点,通过对BaseNP的概念进行一系列的扩展,深入研究了基于不同层次概念的BaseNP对句法分析的影响并探讨更适合中文句法分析的BaseNP定义。利用改进的句法分析模型进行中文句法分析实验,实验结果表明,改进模型可以缩短分析时间26%,提高F值4.4个百分点,交叉括号平均减少18%。After an analysis on Dan Bikel＇s Parser which is based on head--driven statistical model, this paper presents some improvements on this distinctive parser for Chinese parsing. Firstly, a separate N-best POS-tagging module is provided to enhance the morphological processing. Secondly, an independent BaseNP identification module is integrated as another preprocessing module to decrease the complexity in Chinese parsing. And according to the characteristic of Chinese, several extended definitions of BaseNP are introduced, demonstrating that a suitable definition to BaseNP can help improve the performance for Chinese parsing. Finally, experiments are conducted for the refined Chinese statistical parser and the results indicate that both the efficiency and accuracy of Chinese parsing are improved significantly.

关 键 词：计算机应用 中文信息处理 中心词驱动PCFG概率模型 基本名词短语 N-Best词性序列 汉语句法分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]