Identifying event sequences using hidden Markov model

被引:0
作者
Wakabayashi, Kei [1 ]
Miura, Takao [1 ]
机构
[1] Hosei Univ, Dept Elect & Elect Engn, 3-7-2 Kajinocho, Tokyo 1848584, Japan
来源
NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, PROCEEDINGS | 2007年 / 4592卷
关键词
topic classification; hidden Markov model;
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
In this paper, we propose a sophisticated technique for classification of topics appeared in documents. There have been many investigation proposed so far, but few investigation which capture contents directly. Here we consider a topics as a sequence of events and a classification problem as segmentation (or tagging) problem based on Hidden Markov Model (HMM). We show some experimental results to see the validity of the method.
引用
收藏
页码:84 / +
页数:2
相关论文
共 11 条
[1]  
ASAHARA M, 2000, EXTENDED MODELS TOOL
[2]  
Barzilay R, 2004, HLT-NAACL 2004: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE, P113
[3]  
BLEI DM, 2001, TOPIC SEGMENTATION A, P343
[4]  
IWASAKI M, 2002, STAT ANAL INCOMPLETE
[5]  
Makkonen J., 2003, P 2003 C N AM CHAPT, P43
[6]  
Manning C., 1999, Foundations of Statistical Natural Language Processing
[7]  
MITCHELL J, 1997, MACH LEARN, P43
[8]  
MULBREGT P, 1998, ICSLP 98, V6, P2519
[9]  
SHIBATA T, 2005, UNSUPERVISED TOPIC I
[10]  
Yamron J., 1998, P DARPA BROADC NEWS