Weakly supervised learning for hedge classification in scientific literature

ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics

Volumn , Issue , 2007, Pages 992-999

  Medlock, Ben ^a   Briscoe, Ted ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC CLASSIFICATION; BIOMEDICAL TEXT; EXPERIMENTAL EVALUATION; SCIENTIFIC LITERATURE; SUPERVISED MACHINE LEARNING; WEAKLY SUPERVISED LEARNING;

COMPUTATIONAL LINGUISTICS; FENCES;

SUPERVISED LEARNING;

EID: 84860508248     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. 3 = alpha (or beta). Technical report, University of Essex Department of Computer Science.
2. Michele Banko and Eric Brill. 2001. Scaling to very very large corpora for natural language disambiguation. In Meeting of the Association for Computational Linguistics, pages 26-33.
3. Avrim Blum and Tom Mitchell. 1998. Combining labelled and unlabelled data with co-training. In Proceedings of COLT' 98, pages 92-100, New York, NY, USA. ACM Press.
4. Jinxiu Chen, Donghong Ji, Chew L. Tan, and Zhengyu Niu. 2006. Relation extraction using label propagation based semi-supervised learning. In Proceedings of ACL'06, pages 129-136.
5. M. Collins and Y. Singer. 1999. Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in NLP and Very Large Corpora.
6. George Hripcsak and Adam Rothschild. 2004. Agreement, the f-measure, and reliability in information retrieval. J Am Med Inform Assoc., 12(3):296-298.
7. K. Hyland. 1994. Hedging in academic writing and eap textbooks. English for Specific Purposes, 13:239-256.
8. Thorsten Joachims. 1999. Making large-scale support vector machine learning practical. In A. Smola B. Schölkopf, C. Burges, editor, Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge, MA.
9. M. Light, X.Y. Qiu, and P. Srinivasan. 2004. The language of bioscience: Facts, speculations, and statements in between. In Proceedings of BioLink 2004 Workshop on Linking Biological Literature, Ontologies and Databases: Tools for Users, Boston, May 2004.
10. David McClosky, Eugene Charniak, and Mark Johnson. 2006. Effective self-training for parsing. In HLTNAACL.
11. Vincent Ng and Claire Cardie. 2003. Weakly supervised natural language learning without redundant views. In Proceedings of NAACL '03, pages 94-101, Morristown, NJ, USA.
12. K. Nigam and R. Ghani. 2000. Understanding the behavior of co-training. In Proceedings of KDD-2000 Workshop on Text Mining.
13. Kamal Nigam, Andrew K. McCallum, Sebastian Thrun, and Tom M. Mitchell. 2000. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3):103-134.
14. Ellen Riloff, Janyce Wiebe, and Theresa Wilson. 2003. Learning subjective nouns using extraction pattern bootstrapping. In Seventh Conference on Natural Language Learning (CoNLL-03). ACL SIGNLL., pages 25-32.
15. Ben Wellner. 2005. Weakly supervised learning methods for improving the quality of gene name normalization data. In Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases, pages 1-8, Detroit, June. Association for Computational Linguistics.
16. JanyceWiebe, TheresaWilson, Rebecca Bruce, Matthew Bell, and Melanie Martin. 2004. Learning subjective language. Comput. Linguist., 30(3):277-308.
17. Roman Yangarber. 2003. Counter-training in discovery of semantic patterns. In Proceedings of ACL'03, pages 343-350, Morristown, NJ, USA.
18. David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of ACL'95, pages 189-196, Morristown, NJ, USA. ACL.
19. Zhu Zhang. 2004. Weakly-supervised relation classification for information extraction. In CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 581-588, New York, NY, USA. ACM Press.