Chunking with Support Vector Machines

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. We apply weighted voting of 8 SVMs-based systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.

[1]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[2]  Eric Brill,et al.  Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging , 1995, CL.

[3]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[4]  Mitchell P. Marcus,et al.  Text Chunking using Transformation-Based Learning , 1995, VLC@ACL.

[5]  Bernhard Schölkopf,et al.  Incorporating Invariances in Support Vector Learning Machines , 1996, ICANN.

[6]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[7]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[8]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[9]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[10]  Erik F. Tjong Kim Sang,et al.  Representing Text Chunks , 1999, EACL.

[11]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[12]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[13]  Masahiko Haruno,et al.  Feature Selection in SVM Text Categorization , 1999, AAAI/IAAI.

[14]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[15]  Walter Daelemans,et al.  Applying System Combination to Base Noun Phrase Identification , 2000, COLING.

[16]  Masaki Murata,et al.  Named Entity Extraction Based on A Maximum Entropy Model and Transformation Rules , 2000, ACL.

[17]  Yuji Matsumoto,et al.  Japanese Dependency Structure Analysis Based on Support Vector Machines , 2000, EMNLP.

[18]  Takehito Utsuro,et al.  Named Entity Chunking Techniques in Supervised Learning for Japanese Named Entity Recognition , 2000, COLING.

[19]  Erik F. Tjong Kim Sang,et al.  Noun Phrase Recognition by System Combination , 2000, ANLP.

[20]  Erik F. Tjong Kim Sang,et al.  Text Chunking by System Combination , 2000, CoNLL/LLL.

[21]  Yuji Matsumoto,et al.  Use of Support Vector Learning for Chunk Identification , 2000, CoNLL/LLL.

[22]  Sabine Buchholz,et al.  Introduction to the CoNLL-2000 Shared Task Chunking , 2000, CoNLL/LLL.

[23]  Hans van Halteren.  Chunking with WPDV Models , 2000, CoNLL/LLL.

[24]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..