摘要
实现一个基于机器学习的中文缺省项识别系统,对语料库进行预处理,选取多个特征及其组合,通过支持向量模型(SVM)构建的缺省识别模型进行中文缺省识别。研究系统在不同句法分析树上的性能。实验结果证明,该识别系统在标准的句法分析树上F值能达到84.01%,在自动句法树上能达到68.22%。
This paper presents a system for ellipsis identification in Chinese which is based on machine learning. The system can be used to select a number of features and feature combinations through preprocessing the corpus. And Chinese ellipsis identification can also be achieved by the ellipsis identification model built by Support Vector Machine（SVM）. The performance of the system in different parser tree is studied as well. Experimental result shows that the system has F value of 84.01 % on the standard parser tree, and 68.22% on automatic sentence parser tree.
出处
《计算机工程》
CAS
CSCD
2012年第22期130-132,共3页
Computer Engineering
基金
国家自然科学基金资助项目(90920004
60970056
61070123
61003153)
江苏省高校自然科学重大基础研究基金资助项目(08KJA520002)
苏州市科技计划基金资助项目(SYG201112)
关键词
缺省
自然语言处理
句法分析树
机器学习
语料
缺省识别
ellipsis
natural language processing
sentence parse tree
machine learning
corpus
ellipsis identification
