UWashington: Negation resolution using machine learning methods

*SEM 2012 - 1st Joint Conference on Lexical and Computational Semantics

Volumn 1, Issue , 2012, Pages 335-339

  White, James Paul ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; RANDOM PROCESSES; SEMANTICS;

CONDITIONAL RANDOM FIELD; F1 SCORES; MACHINE LEARNING METHODS; REGULAR EXPRESSIONS; SIMPLE SYSTEM; TRAINING DATA;

LEARNING SYSTEMS;

EID: 85011943612     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Shashank Agarwal and Hong Yu. 2010. Biomedicalnegation scope detection with conditional random fields. Journal of the American Medical Informatics Association, 17(6), 696-701. doi:10.1136/jamia.2010.003228.
2. Chapman, W. W., Bridewell, W., Hanbury, P., Cooper, G. F., & Buchanan, B. G.. 2001. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics, 34(5), 301-310. doi:10.1006/jbin.2001.1029.
3. Andrew McCallum. 2002. MALLET: A Machine Learning for Language Toolkit. Retrieved from http://mallet.cs.umass.edu.
4. Roser Morante and Eduardo Blanco. 2012. ∗SEM 2012 Shared Task: Resolving the Scope and Focus of Negation. Proceedings of the First Joint Conference on Lexical and Computational Semantics. Presented at the ∗SEM 2012, Montreal, Canada.
5. Roser Morante and Walter Daelemans. 2009. A Metalearning Approach to Processing the Scope of Negation. Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL2009) (pp. 21-29). Boulder, Colorado: Association for Computational Linguistics.
6. Roser Morante and Walter Daelemans. 2012. ConanDoyleneg: Annotation of negation in Conan Doyle stories. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC).
7. Pradeep G. Mutalik, Aniruddha Deshpande, and Prakash M. Nadkarni. 2001. Use of generalpurpose negation detection to augment concept indexing of medical documents: A quantitative study using the UMLS. Journal of the American Medical Informatics Association: JAMIA, 8(6), 598-609.
8. Lior Rokach, Roni Romano, and Oded Maimon. 2008. Negation recognition in medical narrative reports. Information Retrieval, 11(6), 499-538. doi:10.1007/s1079100890610.
9. Buzhou Tang, Xiaolong Wang, Xuan Wang, Bo Yuan, and Shixi Fan. 2010. A Cascade Method for Detecting Hedges and their Scope in Natural Language Text. Proceedings of the Fourteenth Conference on Computational Natural Language Learning (pp. 13-17). Uppsala, Sweden: Association for Computational Linguistics.
10. Qi Zhao, Chengjie Sun, Bingquan Liu, and Yong Cheng. 2010. Learning to Detect Hedges and their Scope Using CRF. Proceedings of the Fourteenth Conference on Computational Natural Language Learning (pp. 100- 105). Uppsala, Sweden: Association for Computational Linguistics.