HMM和CRFs在信息抽取应用中的比较研究

Comparative Study on HMM and CRFs Applying in Information Extraction

在比较HMM和CRFs数学理论的基础上,分别提出基于HMM词角色标注和基于CRFs字角色标注的人名实体抽取模型,并通过开放性测试和实践应用两次验证、比较两者的有效性,从而在实践中证明从理论比较中得出的结论:CRFs较之HMM更适合于解决序列标注或对象分类问题。

This paper brings forward two models for person - name entity extraction based on the comparison of math theory between HMM and CRFs, one using word role label based HMM and the other using character role label based CRFs, then validates and compares the effect of both by open - testing and applying in practice, and thereby proves in practice that CRFs is fitter for sequence labeling and object classifying than HMM.