BOOTSTRAPPING FOR EXTRACTING RELATIONS FROM LARGE CORPORA  

作　　者：Li Weigang Liu Ting Li Sheng 

机构地区：[1]Information Retrieval Laboratory, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

出　　处：《Journal of Electronics(China)》2008年第1期89-96,共8页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No.60503072, No.60575042 and No.60435020).

摘　　要：A new approach of relation extraction is described in this paper. It adopts a bootstrap- ping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous methods, the proposed method has three main advantages: first, it needs less manual intervention; second, more abundant and reasonable information are introduced to represent a relation pattern; third, it reduces the risk of circular dependency occurrence in bootstrapping. Scalable evaluation methodology and metrics are developed for our task with comparable techniques over TianWang 100G corpus. The experimental results show that it can get 90% precision and have excellent expansibility.A new approach of relation extraction is described in this paper. It adopts a bootstrapping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous methods, the proposed method has three main advantages： first, it needs less manual intervention; second, more abundant and reasonable information are introduced to represent a relation pattern; third, it reduces the risk of circular dependency occurrence in bootstrapping. Scalable evaluation methodology and metrics are developed for our task with comparable techniques over TianWang 100G corpus. The experimental results show that it can get 90% precision and have excellent expansibility.

关 键 词：Relation extraction BOOTSTRAPPING PATTERNS TUPLES 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]