摘要
为了解决中文组块分析精度不高和未利用词的语义信息的问题,提出了一种基于条件随机域模型和语义类的中文组块分析方法.该方法通过研究中文组块分析任务及其序列化特性,采用条件随机域模型融合不同类型特征,克服标记偏置问题,将语义词典中抽取的语义类特征应用到中文组块分析中,提高分析精度.实验表明,该方法取得了F值为92.77%的中文组块分析性能,实验进一步还表明了特征模板的选取和训练语料的规模对于分析性能的影响.
To improve the accuracy of Chinese chunking and utilize the semantic information of words,a new Chinese chunking method is proposed based on conditional random fields and semantic classes.Through the analysis of Chinese chunking task and its sequential characteristics,conditional random fields that could incorporate various types of features were applied to overcome the label bias problem.Semantic features were utilized to improve the chunking performance.Experimental results show that the algorithm achieves impressive accuracy of 92.77% in terms of the F-score.A further experiment indicates the effects of feature template selection and training data′s scales on the aspect of chunking performance.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2011年第7期135-139,共5页
Journal of Harbin Institute of Technology
基金
国家自然科学基金资助项目(60903083)
黑龙江省自然科学基金项目(F200936)
黑龙江省高等学校新世纪优秀人才基金资助项目(1155-ncet-008)
关键词
条件随机域
中文组块分析
特征模板
语义词典
conditional random fields
Chinese chunking
feature template
semantic dictionary
