摘要
在中文词法分析中,分词是词性标注必须经历的阶段。为了能在分词阶段就充分利用词性标注的信息和减少两阶段错误的累计,最好的方法是将两个阶段,整合到一个架构中。该文以无向图模型为基础,将分词和词性标注有机地统一在一个序列标注模型中。由于可以采用更深层次的依赖关系作为特征,一体化系统在1998年人民日报语料上取得了97.19%的分词精确率和95.34%的词性标注精确率,是目前同类系统,在这一语料上取得的最好结果。
For Chinese Part-Of-Speech（POS） tagging,word segmentation is a preliminary step.To reduce accumulated errors between two steps and improve the segmentation performance by utilizing POS information,segmentation and POS tagging can be performed simultaneously.In this paper,a joint segmentation and POS tagging system is proposed based on undirected graphical models which can make full use of the dependencies between the two stages.In the joint system,segmenting and tagging are viewed as the sequence labeling;moreover any connected sub-graph can be viewed as a certain dependency which can be used to find the final opinion labeling.The joint model achieves high performances with 97.19% in segmentation precision and 95.34% in POS tagging precision,which are the state-of-art performances for Chinese word segmentation and tagging on 1998-year People＇s Daily corpus.
出处
《电子与信息学报》
EI
CSCD
北大核心
2010年第3期700-704,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金(60773069
60973169)资助课题
关键词
中文分词
词性标注
一体化系统
无向图模型
Chinese words segmentation
Part-Of-Speech （POS） tagging
Joint system
Undirected graphical model
