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. . ting Vi.  Chương 3 Xây dựng một hệ trích chọn tên riêng sử dụng học thống kê  ng mt h n. th dc th  Chương 2 Các kiến thức nền tảng về học thống kê  cn mt s c th perceptron th thu thp d liu, d  p vt ra cho lu n ting Vit c ng d   tional