SVM based learning system for information extraction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3635 LNAI, Issue , 2005, Pages 319-339

  Li, Yaoyong ^a   Bontcheva, Kalina ^a   Cunningham, Hamish ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; DISTRIBUTED COMPUTER SYSTEMS; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; STATISTICAL METHODS;

INFORMATION EXTRACTION (IE); STATE-OF-THE-ART SYSTEMS; SVM-BASED SYSTEMS;

LEARNING SYSTEMS;

EID: 33645996377     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11559887_19     Document Type: Conference Paper

References (28)

1. Bender, O., Och, F. J., Ney, H.: Maximum entropy models for named entity recognition. In Walter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, 148-151. Edmonton, Canada, 2003
2. Califf, M. E.: Relational learning techniques for natural language information extraction. PhD thesis, University of Texas at Austin, 1998
3. Chieu, H. L., Ng, H. T.: A Maximum Entropy Approach to Information Extraction from Semi-Structured and Free Text. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, 786-791, 2002
4. Chieu, H. L., Ng, H. T.: Named entity recognition: A maximum entropy approach using global information. In Proceedings of the 19th International Conference on Computational Linguistics (COLING'02), Taipei, Taiwan, 2002
5. Chieu, H. L., Ng, H. T.: Named entity recognition with a maximum entropy approach. In Walter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, 160-163. Edmonton, Canada, 2003
6. Cimiano, P., Handschuh, S., Staab, S.: Towards the self-Annotating Web. In Proceedings of WWW'04, 2004
7. 2, an adaptive algorithm for information extraction from web related texts. In Proceedings of the IJCAI-2001 Workshop on Adaptive Text Extraction and Mining, Seattle, 2001
8. 2, Rule Induction for Information Extraction Using Linguistic Constraints. Technical Report CS-03-07, Department of Computer Science, University of Sheffield, Sheffield, September 2003
9. Ciravegna, F., Wilks, Y.: Designing adaptive information extraction for the semantic Web in Amilcare. In S. Handschuh and S. Staab, editors, Annotation for the Semantic Web. IOS Press, Amsterdam, 2003
10. Cunningham, H.: Information extraction, automatic. Encyclopedia of Language and Linguistics, 2nd Edition, 2005
11. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A framework and graphical development environment for robust NLP tools and applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02), 2002
12. Curran, J. R., Clark, S.: Language independent NER using a maximum entropy tagger. In Walter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, 164-167. Edmonton, Canada, 2003
13. Florian, R., Ittycheriah, A., Jing, H., Zhang, T.: Named entity recognition through classifier combination. In Walter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, pages 168-171. Edmonton, Canada, 2003
14. Freigtag, D., McCallum, A. K.: Information extraction with HMMs and shrinkage. In Proceedings of Workshop on Machine Learning for Information Extraction, pages 31-36, 1999
15. Freitag, D.: Information extraction from html: Application of a general learning approach. Proceedings of the Fifteenth Conference on Artificial Intelligence AAAI-98, pages 517-523, 1998
16. Freitag, D.: Machine Learning for Information Extraction in Informal Domains. PhD thesis, Carnegie Mellon University, 1998.
17. Freitag, D.: Machine Learning for Information Extraction in Informal Domains. Machine Learning, 39 (2000) 169-202
18. Freitag, D., Kushmerick, N.: Boosted Wrapper Induction. In Proceedings of AAAI 2000, 2000.
19. Isozaki, H., Kazawa, H.: Efficient Support Vector Classifiers for Named Entity Recognition. In Proceedings of the 19th International Conference on Computational Linguistics (COLING'02), pages 390-396, Taipei, Taiwan, 2002
20. Lewis, D. D., Yang, Y., Rose, T. G., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5 (2004) 361-397
21. Li, Y., Shawe-Taylor, J.: The SVM with uneven margins and Chinese document categorization. In Proceedings of The 17th Pacific Asia Conference on Language, Information and Computation (PACLIC17), pages 216-227, Singapore, Oct. 2003.
22. Mayfield, J., McNamee, P., Piatko, C.: Named entity recognition using hundreds of thousands of features. In Walter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, pages 184-187. Edmonton, Canada, 2003.
23. Roth, D., Yih, W. T.: Relational learning via propositional algorithms: an information extraction case study. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), pages 1257-1263, 2001.
24. SAIC. Proceedings of the Seventh Message Understanding Conference (MUC-7). http://www.itl.nist.gov/iaui/894.02/related projects/muc/index.html, 1998.
25. Sang, E. F., Meulder, F. D.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In Walter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, pages 142-147. Edmonton, Canada, 2003.
26. Soderland, S.: Learning information extraction rules for semi-structured and free text. Machine Learning, 34 (1999) 233-272
27. Song, Y., Yi, E., Kim, E., Lee, G. G.: POSBIOTM-NER: a machine learning approach for bio-named entity recognition. In Workshop on a critical assessment of text mining methods in molecular biology, Granada, Spain (http://www.pdg.cnb. uam.es/BioLINK/workshop BioCreative 04/), 2004.
28. Yang, Y., Liu, X.: A re-examination of text categorization methods. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, pages 42-49, 1999.