TAILIEUCHUNG - Báo cáo khoa học: "Sequential Labeling with Latent Variables: An Exact Inference Algorithm and Its Efficient Approximation"

Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming. | Sequential Labeling with Latent Variables An Exact Inference Algorithm and Its Efficient Approximation Xu Sun1 Jun ichi Tsujii Department of Computer Science University of Tokyo Japan School of Computer Science University of Manchester UK National Centre for Text Mining Manchester UK sunxu tsujii @ Abstract Latent conditional models have become popular recently in both natural language processing and vision processing communities. However establishing an effective and efficient inference method on latent conditional models remains a question. In this paper we describe the latent-dynamic inference LDI which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming. Furthermore we describe a straightforward solution on approximating the LDI and show that the approximated LDI performs as well as the exact LDI while the speed is much faster. Our experiments demonstrate that the proposed inference algorithm outperforms existing inference methods on a variety of natural language processing tasks. 1 Introduction When data have distinct sub-structures models exploiting latent variables are advantageous in learning Matsuzaki et al. 2005 Petrov and Klein 2007 Blunsom et al. 2008 . Actually discriminative probabilistic latent variable models DPLVMs have recently become popular choices for performing a variety of tasks with sub-structures . vision recognition Morency et al. 2007 syntactic parsing Petrov and Klein 2008 and syntactic chunking Sun et al. 2008 . Morency et al. 2007 demonstrated that DPLVM models could efficiently learn sub-structures of natural problems and outperform several widely-used conventional models . support vector machines SVMs conditional random fields CRFs and hidden Markov models HMMs . Petrov and Klein 2008 reported on a syntactic parsing task that DPLVM models can learn more compact and accurate grammars than the conventional techniques without latent

• Báo cáo khoa học
• Sequential Labeling
• Latent Variables
• An Exact Inference Algorithm
• Its Efficient Approximation
• scientific reports
• model language
• process natural language
• Semi Supervised Sequential Labeling and Segmentation
• Giga word Scale Unlabeled Data
• Jun Suzuki and Hideki Isozaki
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Japanese Dependency Parsing
• Semi spoken Language
• mô hình ngôn ngữ
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• Phil Hayes
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• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
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• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
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• Gary G
• Hendrlx and William H
• Lewis
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• Barbara J
• Grosz
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• Petrick
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• Salveter David Maier
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• Jeffrey Conklin David D
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• Stuart C
• Shapiro
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