2007 ExtractingRelationsFromText

From GM-RKB
Jump to navigation Jump to search

Subject Headings: Relation Recognition Task, Dependency Grammar-based Relation Recognition Classifier

Notes

Experiments

Cited By

Quotes

References

  • 1. R. J. Mooney, R. C. Bunescu, Mining knowledge from text using information extraction, SIGKDD Explorations (special issue on Text Mining and Natural Language Processing) 7 (1) (2005) 3–10.
  • 2. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, C. Watkins, Text classification using string kernels, Journal of Machine Learning Research 2 (2002) 419–444.
  • 3.C. D. Fellbaum, WordNet: An Electronic Lexical Database, MIT Press, Cambridge, MA, 1998.
  • 4. L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE 77 (2) (1989) 257–286.
  • 5. A. McCallum, D. Freitag, F. Pereira, Maximum entropy Markov models for information extraction and segmentation, in: Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), Stanford, CA, 2000.
  • 6.J. Lafferty, A. McCallum, F. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, in: Proceedings of 18th International Conference on Machine Learning (ICML-2001), Williamstown, MA, 2001, pp. 282–289.
  • 7. C. Blaschke, A. Valencia, Can bibliographic pointers for known biological data be found automatically? protein interactions as a case study, Comparative and Functional Genomics 2 (2001) 196–206.
  • 8. C. Blaschke, A. Valencia, The frame-based module of the Suiseki information extraction system, IEEE Intelligent Systems 17 (2002) 14–20.
  • 9. R. Bunescu, R. Ge, R. J. Kate, E. M. Marcotte, R. J. Mooney, A. K. Ramani, Y. W. Wong, Comparative experiments on learning information extractors for proteins and their interactions, Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents) 33 (2) (2005) 139–155.
  • 10. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, 1998.
  • 11. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.
  • 12. National Institute of Standards and Technology, ACE-Automatic Content Extraction, http://www.nist.gov/speech/tests/ace (2000).
  • 13.M. Steedman, The Syntactic Process, MIT Press, Cambridge, MA, 2000.
  • 14. J. Hockenmaier, M. Steedman, Generative models for statistical parsing with combinatory categorial grammar, in: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL-2002), Philadelphia, PA, 2002, pp. 335–342.
  • 15. R. C. Bunescu, R. J. Mooney, A shortest path dependency kernel for relation extraction, in: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05), Vancouver, BC, 2005, pp. 724–731.
  • 16. M. J. Collins, Three generative, lexicalised models for statistical parsing, in: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL-97), 1997, pp. 16–23.
  • 17. A. Culotta, J. Sorensen, Dependency tree kernels for relation extraction, in: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), Barcelona, Spain, 2004, pp. 423–429.
  • 18. D. Zelenko, C. Aone, A. Richardella, Kernel methods for relation extraction, Journal of Machine Learning Research 3 (2003) 1083–1106.

,

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 ExtractingRelationsFromTextRazvan C. Bunescu
Raymond J. Mooney		Extracting Relations from Text: From Word Sequences to Dependency Pathshttp://www.cs.utexas.edu/users/ml/papers/relations-07.pdf
Retrieved from "http://www.gabormelli.com/RKB/index.php?title=2007_ExtractingRelationsFromText&oldid=810387"