融合语义类信息的句法分析统计模型  

Statistical Syntactic Parsing Model Fusing Semantic Category Information

作　　者：袁里驰[1] 

机构地区：[1]江西财经大学信息管理学院,南昌330013

出　　处：《数据采集与处理》2017年第1期175-181,共7页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61262035;61562034)资助项目;江西省自然科学基金(20142BAB207028)资助项目;江西省科技支撑计划(20151BBE50082)资助项目

摘　　要：稀疏数据严重影响句子结构分析模型的结果,而句法结构是语义内容和句法分析形式的结合。本文在语义结构信息标注的基础上提出了一种基于语义搭配关系的词聚类模型和算法,建立基于语义类的头驱动句子结构分析统计模型。该语言模型不但比较成功地解决了数据稀疏问题,而且句子结构分析系统性能也有了明显的提高。句子结构分析实验结果表明,基于语义类的头驱动的句子结构分析统计模型,其召回率和精确率的值相应为88.26%和88.73%,综合指标改进了8.39%。Data sparseness severely affects the system performances of syntactic parsing,and syntactic structures are unities of syntactic forms and semantic contents.Based on the labeling of semantic information,a word clustering model and algorithm is proposed.And a head-driven statistical syntactic parsing model based on semantic category is established.The problem of data sparseness is successfully solved,and the system performances of syntactic parsing are obviously enhanced.Experiments are conducted for the head-driven statistical syntactic parsing model based on semantic category.It achieves 88.73% precision and 88.26% recall.F measure is improved 8.39% compared with the distinctive head-driven parsing model.

关 键 词：句子结构分析统计模型 语义角色标注 词的自动聚类 头驱动 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]