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https://dl.acm.org/citation.cfm?id=1073361
We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality.Cited by: 687
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.4709
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.5241
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We apply Support Vector Machines (SVMs) to identify base noun phrases in sentences. SVMs are known to achieve high generalization performance even in high dimensional feature space. We explore two different chunk representations (IOB and open/close brackets) and use a two-layer system approach for the …
https://pdfs.semanticscholar.org/6a47/3e9e0a2183928b2d78bddf4b3d01ff46c454.pdf
Chunking with Support Vector Machines Taku Kudo and Yuji Matsumoto Graduate School of Information Science, Nara Institute of Science and Technology ftaku-ku,[email protected] Abstract We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization perfor-
https://link.springer.com/chapter/10.1007/11880592_27
Support Vector Machine (SVM-based) phrase chunking system had been shown to achieve high performance for text chunking. But its inefficiency limits the actual use on large dataset that only handles several thousands tokens per second.Cited by: 1
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.8048
Tagging can also be seen as a multi-class classification problem. After recasting the multi-class problem as a number of binary-class problems, we use support vector machines to implement the binary classifiers. We explore two semantic chunking tasks. In the first task we simultaneously detect the target word and segments of semantic roles.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.540.130
A chunking procedure utilized in for linear classifiers is proposed here for nonlinear kernel classification of massive datasets. A highly accurate algorithm based on nonlinear support vector machines that utilizes a linear programming formulation is developed here as a completely unconstrained minimization problem.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.560
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems.
http://chasen.org/~taku/publications/naacl2001-slide.pdf
Chunking with Support Vector Machines Graduate School of Information Science, Nara Institute of Science and Technology, JAPAN Taku Kudo, Yuji Matsumoto ftaku-ku,[email protected]. Chunking (1/2) ... † We proposed a general framework for chunking based on SVMs.
https://dl.acm.org/doi/10.3115/1073336.1073361
Chunking with support vector machines. Pages 1–8. Previous Chapter Next Chapter. ABSTRACT. We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry ...
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