ng h th
n ting Vit bc
thng k

Nguyn Th 

i h
Lu : 1 01 10
ng dn: TS Nguy
o v: 2007


Abstract: M t ng h 
m cc thng
ng thng h 
n ting Vi ng
mt h n ting Vit s d CRF++ ca
t s kt qa thc nghic

Keywords: , Thu, c, X 


Content
MỞ ĐẦU
 









 .  , 


, 





 , 





 /










, 

























.


, 




mt s gi 








 . 










nhau, 





 th ting h tr
n bn ting Vit.  m cc th thu thp d liu,
d  p vt ra cho lu
n ting Vit c ng d 


tional
Random Fields (CRF- Laferty, 2001) thu perceptron  d liu
dng chui (M.Collins, 2002). 














 ,  















 hun luyn. 


















.
Luc t chu:
 Chương 1 Tổng quan
  ng h 
 cc sc tin ca h n
ca hng ca h a chn p trong tng
ng hp c thng thi trong pha lu cc v 
n ving h th

dc th
 Chương 2 Các kiến thức nền tảng về học thống kê
 cn mt s c th
perceptron.              m ca tng
 ng s tp trung ving h  chn
ting Vi.
 Chương 3 Xây dựng một hệ trích chọn tên riêng sử dụng học thống kê
 ng mt h n
ting Vit s dg c CRF++ ct s kt qu thc nghim ca
c.

References
[1]. Duglas E.Appelt, D.J.Israel. Introduction to Information Extraction Technology.
1999.
[2]. A.Berger. The Improved Iterative Scaling Algorithm: A gentle Introdution. School
of Computer Science, Carnegie Mellon University. 1999.
[3]. M.Collins. Discriminative Training Methods for Hidden Markov Models: Theory
and Experiment with Perceptron Algorithms.2002.
[4]. J.Cowie, W.Lehnert. Information Extraction. Paper. 1996
[5]. R.Dugad, U.B.Desai - "A Tutorial on Hidden Markov Model" - Technical Report
No: SPANN-96.1, Indian Institute of Technology.1996.
[6]. D.Freitag, S.Khadivi. .A Sequence Alignment Model Based on the Averaged
Perceptron. 2006.
[7]. Freund & Schapire. Large Margin Classification Using the perceptron Algorithm.
Machine Learning 37(3) 277-296, 1999.
[8]. J.Lafferty, A.McCallum, and F.Pereira. Conditional random fields: probabilistic
models for segmenting and labeling sequence data. In Proc. ICML, 2001.
[9]. Dong C.Liu and Jorge Nocedal. On the limited memory BFGS method for large
scale optimization.Mathematical Programming 45 (1989),pp.503-528.
[10]. Walter F.Mascarenhas. The BFGS method with exact line searches fails for non-

convex objective functions. Published May 7, 2003.
[11]. A. McCallum, K. Rohanimanesh, and C. Sutton. Dynamic Conditional Random
Fields for Jointly Labeling Multiple Sequences. 2004
[12]. A.McCallum, C.Shutton. An introduction for Conditional Random Fields for
Relational Learning. 2005
[13]. A.McCallum, D.Freitag, and F. Pereira. Maximum entropy markov models for
information extraction and segmentation. In Proc. Iternational Conference on
Mechine Learning, 2000, pages 591-598.
[14]. A.McCallum, W.li. Early Results for Named Entity Recognition with Conditional
Random Fields, Feature Induction and Web-Enhanced Lexicons. 2003.
[15]. A.McCallum. Efficiently Inducing Features of Conditional Random Fields. 2003.
[16]. A.B.Poritz - "Hidden Markov Models - A Guide Tour" - IEEE, 1988.
[17]. L.R.Rabiner - "A Tutorial on Hidden Markov Models and Selected Applications in
Speech Recognition" - Proceedings of IEEE, VOL.77, NO.2, FEB 1989.
[18]. A.Ratnaparkhi.A maximum entropy model for part-of-speech tagging.In Proc.
Emparical Methods for Natural Language Processing, 1996.
[19]. B.Roask, M.Saraclar, M.Collins, M.Johnson. Discriminative Language Modeling
with Conditional Random Fields and the Perceptron Algorithm. 2004.
[20]. Sunita Sarawagi, William W. Cohen. Semi-Markov Conditional Random Fields for
Information Extraction. 2004.
[21]. H.Wallach. Efficient Training of Conditional Random Fields. University Of
Edinburgh, 2002.
[22]. Y.Zhang, S.Clark. Chinese Segmentation with a Word-Based Perceptron Algorithm.
2006.
[23]. n dn Ting Vit. 1999.
[24]. Nguyn Cn bii thc th tronn Ting Vit nhm h tr
Web ng ng thc th. 2005.
[25]. Nguyn C, 

y. Named Entity

Recognition in Vietnamese Free-Text and Web Documents Using Conditional
Random Fields. 2005
[26]. Tri Tran Q., Thao Pham T.X., Hung Ngo Q., Dien Dinh and Niegl Collier. Named
Entitiy Recognition in Vietnamese Document. 2007.