HMM-based Korean named entity recognition for information extraction

被引：0
|
作者：
Yun, Bo-Hyun [1 ]
机构：
[1] Mokwon Univ, Dept Comp Educ, Taejon 302729, South Korea
来源：
KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT | 2007年 / 4798卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents the HMM (Hidden Markov Model) based named entity recognition method for information extraction. In Korean language, named entities have the distinct characteristics unlike other languages. Many named entities can be decomposed into more than one word. Moreover, there are contextual relationship between named entities and their surrounding words. There are many internal and external evidences in named entities. To overcome data sparseness problem, we used multi-level back-off methods. The experimental result shows the F-measure of 87.6% in the economic article domain.
引用
收藏
页码：526 / 531
页数：6
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