Japanese Dependency Structure Analysis Based on Support Vector Machines

Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, SIGDAT-EMNLP 2000 - Held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics, ACL 2000

Volumn , Issue , 2000, Pages 18-25

  Kudo, Taku ^a   Matsumoto, Yuji ^a  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; DECISION TREES; LEARNING ALGORITHMS; MAXIMUM ENTROPY METHODS; NATURAL LANGUAGE PROCESSING SYSTEMS; SYNTACTICS;

DEPENDENCY STRUCTURES; GENERALIZATION PERFORMANCE; HIGH-DIMENSIONAL FEATURE SPACE; INPUT DATAS; LEARNING FRAMEWORKS; MACHINE-LEARNING; MAXIMUM ENTROPY MODELS; ON-MACHINES; STRUCTURE ANALYSIS; SUPPORT VECTORS MACHINE;

SUPPORT VECTOR MACHINES;

EID: 85123780051     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Eugene Charniak. 2000. A maximum-entropy-inspired parser. In Processing of the NAACL 2000, pages 132-139.
2. Michael Collins. 1996. A new statistical parser based on bigram lexical dependencies. In Proceedings of the ACL '96, pages 184-191.
3. C. Cortes and Vladimir N. Vapnik. 1995. Support Vector Networks. Machine Learning, 20:273-297.
4. Y. Freund and Schapire. 1996. Experiments with a new Boosting algoritm. In 13th International Conference on Machine Learning.
5. Masakazu Fujio and Yuji Matsumoto. 1998. Japanese Dependency Structure Analysis based on Lexicalized Statistics. In Proceedings of EMNLP '98, pages 87-96.
6. Msahiko Haruno, Satoshi Shirai, and Yoshifumi Ooyama. 1998. Using Decision Trees to Construct a Partial Parser. In Proceedings of the COLING '98, pages 505-511.
7. Takashi Inui and Kentaro Inui. 2000. Committe-based Decision Making in Probabilistic Partial Parsing. In Proceedings of the COLING 2000, pages 348-354.
8. Thorsten Joachims. 1998. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In European Conference on Machine Learning (ECML).
9. Sadao Kurohashi and Makoto Nagao. 1997. Kyoto University text corpus project. In Proceedings of the ANLP, Japan, pages 115-118.
10. John C. Platt. 1999. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In Advances in Large Margin Classifiers. MIT Press.
11. Adwait Ratnaparkhi. 1997. A Liner Observed Time Statistical Parser Based on Maximum Entropy Models. In Proceedings of EMNLP '97.
12. Satoshi Sekine, Kiyotaka Uchimoto, and Hitoshi Isahara. 2000. Backward Beam Search Algorithm for Dependency Analysis of Japanese. In Proceedings of the COLING 2000, pages 754-760.
13. Hirotoshi Taira and Masahiko Haruno. 1999. Feature Selection in SVM Text Categorization. In AAAI-99.
14. Kiyotaka Uchimoto, Satoshi Sekine, and Hitoshi Isahara. 1999. Japanese Dependency Structure Analysis Based on Maximum Entropy Models. In Proceedings of the EACL, pages 196-203.
15. Vladimir N. Vapnik. 1998. Statistical Learning Theory. Wiley-Interscience.