摘要
全文检索是一种有效的信息检索技术,改进最大熵模型的应用研究在自然语言处理领域中受到关注。笔者利用语料库中词性标注和词频标注的上下文信息,建立基于互信息的最大熵方法的检索系统,研究的重点在于提出一种中文分词算法,并从实际问题出发,采用面向对象和模型驱动的设计方法,将该算法应用于实际的全文检索系统的设计中。
Chinese full text information retrivial is one of the efficient technologicies of information retrivial, and a lot of researches have been made on the application of the maximum entropy modeling in the natural language processing during recent years. This paper presents a retrivial system based on maximum entropy principle. The context of speech tagging and word frequencytagging included in database are considered,and Chinese word segmentation algorithm is put forward. By adopting objectoriented and the model driven programming method the algorithm can be used in a system design.
出处
《河北科技大学学报》
CAS
北大核心
2009年第2期112-115,共4页
Journal of Hebei University of Science and Technology
基金
河北省科技厅科技攻关项目(07213574)
关键词
全文检索
最大熵模型
分词
Chinese full text information retrivial
maximum entropy principle
word segmentation algorithm
