Automatic discourse connective detection in biomedical text

Journal of the American Medical Informatics Association

Volumn 19, Issue 5, 2012, Pages 800-808

  Ramesh, Balaji Polepalli ^a,b   Prasad, Rashmi ^b   Miller, Tim ^b   Harrington, Brian ^a,b   Yu, Hong ^a,b  

^a University of Wisconsin Milwaukee   (United States)

^b UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; INFORMATION PROCESSING; MACHINE LEARNING; MEDICAL INFORMATION SYSTEM; MEDICAL LITERATURE; SCORING SYSTEM; SUPPORT VECTOR MACHINE; WORD LIST RECALL; ARTIFICIAL INTELLIGENCE; DATA MINING; HUMAN; METHODOLOGY; NATURAL LANGUAGE PROCESSING;

ARTIFICIAL INTELLIGENCE; DATA MINING; HUMANS; NATURAL LANGUAGE PROCESSING; SUPPORT VECTOR MACHINES;

EID: 84872244355     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000775     Document Type: Article

References (46)

1. Zheng J, Chapman WW, Crowley RS, et al. Coreference resolution: a review of general methodologies and applications in the clinical domain. J Biomed Inform 2011;44:1113-22.
2. Prasad R, Dinesh N, Lee A, et al. The penn discourse treebank 2.0. Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC 2008). Marrakech, Morocco, 2008:2961-8. Organised by ELRA, the European Language Resources Association, with the collaboration of major international institutions and organisations.
3. Marcu D. Improving summarization through rhetorical parsing tuning. In: The 6th Workshop on Very Large Corpora; Montreal, Canada. New Brunswick, NY: The Association for Computational Linguistics SIGDAT, 1998:206-15.
4. Hovy EH. Automated discourse generation using discourse structure relations. In: Artificial Intelligence, Volume 63. 1993:341-85.
5. Hernault H, Piwek P, Prendinger H, et al. Generating dialogues for virtual agents using nested textual coherence relations. Proceedings of the 8th International Conference on Intelligent Virtual Agents; Tokyo, Japan. Tokyo, Japan: International Information Science Foundation, 2008:139-45.
6. McDonald R, Hannan K, Neylon T, et al. Structured models for fine-to-coarse sentiment analysis. Annual Meeting-Association For Computational Linguistics, Volume 45; Prague, Czech Republic. New Brunswick, NY: The Association for Computational Linguistics, 2007:432.
7. Mannem P, Prasad R, Joshi A. Question generation from paragraphs at UPenn: QGSTEC system description. Proceedings of QG2010: The Third Workshop on Question Generation. Pittsburgh, Pennsylvania, USA, 2010:84-91.
8. MacCartney B, Manning CD. Natural logic for textual inference. Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing. Prague, Czech Republic, 2007:193-200.
9. Mani I, Verhagen M, Wellner B, et al. Machine learning of temporal relations. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics; Sydney, Australia. New Brunswick, NY: The Association for Computational Linguistics, 2006:753-60.
10. Prasad R, Joshi A. A discourse-based approach to generating why-questions from texts. Proceedings of the Workshop on the Question Generation Shared Task and Evaluation Challenge. Arlington, VA: National Science Foundation, 2008.
11. Coden A, Savova G, Sominsky I, et al. Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model. J Biomed Inform 2009;42:937-49.
12. Roberts A, Gaizauskas R, Hepple M, et al. Building a semantically annotated corpus of clinical texts. J Biomed Inform 2009;42:950-66.
13. Soricut R, Marcu D. Sentence level discourse parsing using syntactic and lexical information. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. 2003:149-56.
14. Duverle DA, Prendinger H. A novel discourse parser based on support vector machine classification. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2; Singapore. New Brunswick, NY: The Association for Computational Linguistics, 2009:665-73.
15. Subba R, Di Eugenio B. An effective discourse parser that uses rich linguistic information. Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics; Singapore. New Brunswick, NY: The Association for Computational Linguistics, 2009:566-74.
16. Wellner B, Pustejovsky J. Automatically identifying the arguments of discourse connectives. Proceedings of EMNLP-CoNLL; Prague, Czech Republic. New Brunswick, NY: The Association for Computational Linguistics SIGDAT and The Association for Computational Linguistics SIGNLL, 2007:92-101.
17. Elwell R, Baldridge J. Discourse connective argument identification with connective specific rankers. The IEEE International Conference on Semantic Computing. Santa Clara, CA, USA, 2008:198-205.
18. Pitler E, Nenkova A. Using syntax to disambiguate explicit discourse connectives in text. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers; Singapore. New Brunswick, NY: The Association for Computational Linguistics, 2009:13-16.
19. Light M, Qiu XY, Srinivasan P. The language of bioscience: facts, speculations, and statements in between. Proceedings of BioLink 2004 Workshop on Linking Biological Literature, Ontologies and Databases: Tools for Users. Boston, Massachusetts, 2004:17-24.
20. Mullen T, Mizuta Y, Collier N. A baseline feature set for learning rhetorical zones using full articles in the biomedical domain. In: ACM SIGKDD Explorations Newsletter, Volume 7. New York, NY, USA: ACM, 2005:52-8.
21. Teufel S, Siddharthan A, Batchelor C. Towards discipline-independent argumentative zoning: evidence from chemistry and computational linguistics. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3; Singapore. New Brunswick, NY: The Association for Computational Linguistics SIGDAT, 2009:1493-502.
22. Biber D, Jones JK. Merging corpus linguistic and discourse analytic research goals: discourse units in biology research articles. Corpus Linguistics and Linguistic Theory 2005;1:151-82.
23. Szarvas G, Vincze V, Farkas R, et al. The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts. Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing; Columbus, Ohio. New Brunswick, NY: The Association for Computational Linguistics, 2008:38-45.
24. Agarwal S, Yu H. Biomedical negation scope detection with conditional random fields. J Am Med Inform Assoc 2010;17:696.
25. Agarwal S, Yu H. Detecting hedge cues and their scope in biomedical literature with conditional random fields. J Biomed Inform 2010;43:953-61.
26. Prasad R, McRoy S, Frid N, et al. The biomedical discourse relation bank. BMC Bioinformatics 2011;12:188.
27. Yu H, Frid N, McRoy S, et al. A pilot annotation to investigate discourse connectivity in biomedical text. Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing; Columbus, Ohio. New Brunswick, NY: The Association for Computational Linguistics, 2008:92-3.
28. Rizomilioti V. Exploring epistemic modality in academic discourse using corpora. Information technology in languages for specific purposes 2006;7:53-71.
29. Lisacek F, Chichester C, Kaplan A, et al. Discovering paradigm shift patterns in biomedical abstracts: application to neurodegenerative diseases. In: First International Symposium on Semantic Mining in Biomedicine. Hinxton, Cambridgeshire, UK, 2005:11-13. Organised by the EU Network of Excellence Semantic Mining and the European Bioinformatics Institute (EBI).
30. Savova GK, Chapman WW, Zheng J, et al. Anaphoric relations in the clinical narrative: corpus creation. J Am Med Inform Assoc 2011;18:459.
31. Castano J, Zhang J, Pustejovsky J. Anaphora resolution in biomedical literature. Proceedings of the International Symposium on Reference Resolution for NLP. Alicante, Spain, 2002.
32. Ramesh BP, Yu H. Identifying discourse connectives in biomedical text. AMIA Ann Symp Proc 2010;2010:657-61.
33. Marcus MP, Marcinkiewicz MA, Santorini B. Building a large annotated corpus of English: the penn treebank. Comput Ling 1993;19:313-30.
34. Palmer M, Gildea D, Kingsbury P. The proposition bank: an annotated corpus of semantic roles. Comput Ling 2005;31:71-106.
35. Weischedel R, Palmer M, Marcus M, et al. Ontonotes Release 4.0. Philadelphia: Linguistic Data Consortium, 2011, Technical Report.
36. Kim JD, Ohta T, Tateisi Y, et al. GENIA corpusda semantically annotated corpus for bio-textmining. Bioinformatics 2003;19(Suppl 1):i180-2.
37. Verspoor K, Cohen KB, Hunter L. The textual characteristics of traditional and Open Access scientific journals are similar. BMC Bioinformatics 2009;10:183.
38. Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning. Williamstown, MA, USA, 2001:282-9.
39. Settles B. ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics 2005;21:3191.
40. Tong S, Koller D. Support vector machine active learning with applications to text classification. J Mach Learn Res 2002;2:45-66.
41. Joachims T. Text categorization with support vector machines: learning with many relevant features. In: Machine Learning: ECML-98. Chemnitz, Germany, 1998:137-42.
42. McClosky D. Any domain parsing: automatic domain adaptation for natural language parsing. In: Ph. D. thesis, Department of Computer Science, Brown University. Providence, Rhode Island, USA, 2009.
43. Leaman R, Gonzalez G. BANNER: an executable survey of advances in biomedical named entity recognition. Pac Symp Biocomput 2008;13:652-63.
44. Gerner M, Nenadic G, Bergman CM. LINNAEUS: a species name identification system for biomedical literature. BMC Bioinformatics 2010;11:85.
45. Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004;32:D267-70.
46. Dahlmeier D, Ng HT. Domain adaptation for semantic role labeling in the biomedical domain. Bioinformatics 2010;26:1098-104