摘要： 针对信息抽取领域中存在的抽取结果难以满足需要的问题，给出基于条件随机域模型的方法，以解决组块标注和实体关系抽取问题。通过定义中文组块和实体关系的标注方式，选择比较通用的《人民日报》语料，训练出效率较高的二阶模板来抽取文本中的实体关系。实验结果表明，该方法可以获得更好的抽取效果。

关键词: 信息抽取, 组块标注, 实体关系抽取, 条件随机域模型

Abstract: To solve disorder among information items and lack of information item in the field of information extraction, this paper proposes a solution to deal with chunks labeling and Entity Relation Extraction(ERE) based on the conditional random fields model. This paper defines the representation of Chinese chunk and entity relation, and uses label dataset of “People’s Daily” as sample dataset to train an optimized model for the entity extraction. Experimental results show this method has better extraction performance.

Key words: information extraction, chunks labeling, entity relation extraction, Conditional Random Fields(CRFs) model

中图分类号: 

• TP391