Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Regression Applied to Semantic Textual Similarity
Semantic textual similarity（STS） is a common task in natural language processing（NLP）. STS measures the degree of semantic equivalence of two textual snippets. Recently, machine learning methods have been applied to this task, including methods based on support vector regression（SVR）. However, there...
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| Published in | Shanghai jiao tong da xue xue bao Vol. 20; no. 2; pp. 143 - 148 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Shanghai Jiaotong University Press
01.04.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-1172 1995-8188 |
| DOI | 10.1007/s12204-015-1602-2 |
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| Abstract | Semantic textual similarity（STS） is a common task in natural language processing（NLP）. STS measures the degree of semantic equivalence of two textual snippets. Recently, machine learning methods have been applied to this task, including methods based on support vector regression（SVR）. However, there exist amounts of features involved in the learning process, part of which are noisy features and irrelative to the result.Furthermore, different parameters will significantly influence the prediction performance of the SVR model. In this paper, we propose genetic algorithm（GA） to select the effective features and optimize the parameters in the learning process, simultaneously. To evaluate the proposed approach, we adopt the STS-2012 dataset in the experiment. Compared with the grid search, the proposed GA-based approach has better regression performance. |
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| AbstractList | Semantic textual similarity (STS) is a common task in natural language processing (NLP). STS measures the degree of semantic equivalence of two textual snippets. Recently, machine learning methods have been applied to this task, including methods based on support vector regression (SVR). However, there exist amounts of features involved in the learning process, part of which are noisy features and irrelative to the result. Furthermore, different parameters will significantly influence the prediction performance of the SVR model. In this paper, we propose genetic algorithm (GA) to select the effective features and optimize the parameters in the learning process, simultaneously. To evaluate the proposed approach, we adopt the STS-2012 dataset in the experiment. Compared with the grid search, the proposed GA-based approach has better regression performance. Semantic textual similarity（STS） is a common task in natural language processing（NLP）. STS measures the degree of semantic equivalence of two textual snippets. Recently, machine learning methods have been applied to this task, including methods based on support vector regression（SVR）. However, there exist amounts of features involved in the learning process, part of which are noisy features and irrelative to the result.Furthermore, different parameters will significantly influence the prediction performance of the SVR model. In this paper, we propose genetic algorithm（GA） to select the effective features and optimize the parameters in the learning process, simultaneously. To evaluate the proposed approach, we adopt the STS-2012 dataset in the experiment. Compared with the grid search, the proposed GA-based approach has better regression performance. |
| Author | 苏柏桦 王英林 |
| AuthorAffiliation | Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China Department of Computer Science and Technology, Shanghai University of Finance and Economics, Shanghai 200433, China |
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| Cites_doi | 10.1145/1961189.1961199 10.1109/TAI.2003.1250182 10.1016/j.eswa.2005.09.024 10.1016/S0004-3702(97)00043-X 10.1023/A:1009752403260 10.1080/10556789208805504 10.1007/978-1-4757-3264-1 10.1023/B:STCO.0000035301.49549.88 |
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| DocumentTitleAlternate | Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Regression Applied to Semantic Textual Similarity |
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| Keywords | TP 311.5 support vector regression (SVR) semantic textural similarity (STS) feature selection |
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| Notes | 31-1943/U support vector regression（SVR）,feature selection,semantic textural similarity（STS） SU Bai-hua, WANG Ying-lin（1.Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China ; 2. Department of Computer Science and Technology, Shanghai University of Finance and Economics, Shanghai 200433, China） Semantic textual similarity（STS） is a common task in natural language processing（NLP）. STS measures the degree of semantic equivalence of two textual snippets. Recently, machine learning methods have been applied to this task, including methods based on support vector regression（SVR）. However, there exist amounts of features involved in the learning process, part of which are noisy features and irrelative to the result.Furthermore, different parameters will significantly influence the prediction performance of the SVR model. In this paper, we propose genetic algorithm（GA） to select the effective features and optimize the parameters in the learning process, simultaneously. To evaluate the proposed approach, we adopt the STS-2012 dataset in the experiment. Compared with the grid search, the proposed GA-based approach has better regression performance. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| References | Huang, Wang (CR4) 2006; 31 Hsu, Chang, Lin (CR16) 2003 Chang, Lin (CR17) 2011; 2 Witten, Frank (CR7) 2005 Smola, Schölkopf (CR11) 2004; 14 Bennett, Mangasarian (CR13) 1992; 1 Vapnik, Golowich, Smola (CR12) 1997; 9 Kohavi, John (CR6) 1997; 97 Bär, Biemann, Gurevych (CR2) 2012 Schölkopf, Smola (CR10) 2002 Drucker, Burges, Kaufman (CR9) 1997; 9 Vapnik (CR8) 2000 Cortes, Vapnik (CR14) 1995; 20 Salzberg (CR15) 1997; 1 Fröhlich, Chapelle, Schölkopf (CR3) 2003 Šarić, Glavaš, Karan (CR1) 2012 John, Kohavi, Pfleger (CR5) 1994 H Drucker (1602_CR9) 1997; 9 V Vapnik (1602_CR8) 2000 B Schölkopf (1602_CR10) 2002 S L Salzberg (1602_CR15) 1997; 1 V Vapnik (1602_CR12) 1997; 9 G H John (1602_CR5) 1994 I H Witten (1602_CR7) 2005 C W Hsu (1602_CR16) 2003 H Fröhlich (1602_CR3) 2003 C Cortes (1602_CR14) 1995; 20 C C Chang (1602_CR17) 2011; 2 A J Smola (1602_CR11) 2004; 14 R Kohavi (1602_CR6) 1997; 97 F Šarić (1602_CR1) 2012 C L Huang (1602_CR4) 2006; 31 K P Bennett (1602_CR13) 1992; 1 D Bär (1602_CR2) 2012 |
| References_xml | – volume: 9 start-page: 281 year: 1997 end-page: 287 ident: CR12 article-title: Support vector method for function approximation, regression estimation, and signal processing [J] publication-title: Advances in Neural Information Processing Systems – start-page: 121 year: 1994 end-page: 129 ident: CR5 article-title: Irrelevant features and the subset selection problem [C] publication-title: Proceedings on Machine Learning’94 – year: 2003 ident: CR16 publication-title: A practical guide to support vector classification [R] – volume: 2 start-page: 27 issue: 3 year: 2011 ident: CR17 article-title: LIBSVM: A library for support vector machines [J] publication-title: ACM Transactions on Intelligent Systems and Technology doi: 10.1145/1961189.1961199 – start-page: 142 year: 2003 end-page: 148 ident: CR3 article-title: Feature selection for support vector machines by means of genetic algorithm [C] publication-title: Proceedings of 15th IEEE International Conference on Tools with Artificial Intelligence doi: 10.1109/TAI.2003.1250182 – volume: 31 start-page: 231 issue: 2 year: 2006 end-page: 240 ident: CR4 article-title: A GA-based feature selection and parameters optimization for support vector machines [J] publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2005.09.024 – year: 2005 ident: CR7 publication-title: Data mining: Practical machine learning tools and techniques [M] – start-page: 435 year: 2012 end-page: 440 ident: CR2 article-title: Ukp: Computing semantic textual similarity by combining multiple content similarity measures [C] publication-title: Proceedings of the First Joint Conference on Lexical and Computational Semantics – volume: 97 start-page: 273 issue: 1 year: 1997 end-page: 324 ident: CR6 article-title: Wrappers for feature subset selection [J] publication-title: Artificial Intelligence doi: 10.1016/S0004-3702(97)00043-X – volume: 1 start-page: 317 issue: 3 year: 1997 end-page: 328 ident: CR15 article-title: On comparing classifiers: Pitfalls to avoid and a recommended approach [J] publication-title: Data Mining and Knowledge Discovery doi: 10.1023/A:1009752403260 – volume: 20 start-page: 273 issue: 3 year: 1995 end-page: 297 ident: CR14 article-title: Support-vector networks [J] publication-title: Machine Learning – year: 2002 ident: CR10 publication-title: Learning with kernels: Support vector machines, regularization, optimization, and beyond [M] – volume: 1 start-page: 23 issue: 1 year: 1992 end-page: 34 ident: CR13 article-title: Robust linear programming discrimination of two linearly inseparable sets [J] publication-title: Optimization Methods and Software doi: 10.1080/10556789208805504 – year: 2000 ident: CR8 publication-title: The nature of statistical learning theory [M] doi: 10.1007/978-1-4757-3264-1 – start-page: 441 year: 2012 end-page: 448 ident: CR1 article-title: Takelab: Systems for measuring semantic text similarity [C] publication-title: Proceedings of the First Joint Conference on Lexical and Computational Semantics – volume: 14 start-page: 199 issue: 3 year: 2004 end-page: 222 ident: CR11 article-title: A tutorial on support vector regression [J] publication-title: Statistics and Computing doi: 10.1023/B:STCO.0000035301.49549.88 – volume: 9 start-page: 155 year: 1997 end-page: 161 ident: CR9 article-title: Support vector regression machines [J] publication-title: Advances in Neural Information Processing Systems – start-page: 121 volume-title: Proceedings on Machine Learning’94 year: 1994 ident: 1602_CR5 – volume: 97 start-page: 273 issue: 1 year: 1997 ident: 1602_CR6 publication-title: Artificial Intelligence doi: 10.1016/S0004-3702(97)00043-X – start-page: 142 volume-title: Proceedings of 15th IEEE International Conference on Tools with Artificial Intelligence year: 2003 ident: 1602_CR3 doi: 10.1109/TAI.2003.1250182 – start-page: 441 volume-title: Proceedings of the First Joint Conference on Lexical and Computational Semantics year: 2012 ident: 1602_CR1 – volume-title: A practical guide to support vector classification [R] year: 2003 ident: 1602_CR16 – start-page: 435 volume-title: Proceedings of the First Joint Conference on Lexical and Computational Semantics year: 2012 ident: 1602_CR2 – volume: 9 start-page: 281 year: 1997 ident: 1602_CR12 publication-title: Advances in Neural Information Processing Systems – volume: 14 start-page: 199 issue: 3 year: 2004 ident: 1602_CR11 publication-title: Statistics and Computing doi: 10.1023/B:STCO.0000035301.49549.88 – volume-title: The nature of statistical learning theory [M] year: 2000 ident: 1602_CR8 doi: 10.1007/978-1-4757-3264-1 – volume: 2 start-page: 27 issue: 3 year: 2011 ident: 1602_CR17 publication-title: ACM Transactions on Intelligent Systems and Technology doi: 10.1145/1961189.1961199 – volume-title: Learning with kernels: Support vector machines, regularization, optimization, and beyond [M] year: 2002 ident: 1602_CR10 – volume: 1 start-page: 23 issue: 1 year: 1992 ident: 1602_CR13 publication-title: Optimization Methods and Software doi: 10.1080/10556789208805504 – volume: 9 start-page: 155 year: 1997 ident: 1602_CR9 publication-title: Advances in Neural Information Processing Systems – volume: 31 start-page: 231 issue: 2 year: 2006 ident: 1602_CR4 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2005.09.024 – volume-title: Data mining: Practical machine learning tools and techniques [M] year: 2005 ident: 1602_CR7 – volume: 1 start-page: 317 issue: 3 year: 1997 ident: 1602_CR15 publication-title: Data Mining and Knowledge Discovery doi: 10.1023/A:1009752403260 – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 1602_CR14 publication-title: Machine Learning |
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| SubjectTerms | Architecture Computer Science Electrical Engineering Engineering Genetic algorithms Learning Life Sciences Materials Science Mathematical analysis Mathematical models Regression Semantics Similarity Tasks |
| Title | Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Regression Applied to Semantic Textual Similarity |
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