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%。

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

Abstract: 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.

Key words: ellipsis, natural language processing, sentence parse tree, machine learning, corpus, ellipsis identification

中图分类号: 

• TP391