Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation

Journal of the American Medical Informatics Association

Volumn 20, Issue 5, 2013, Pages 931-939

  Ferraro, Jeffrey P ^a,b   Daumé III, Hal ^c   DuVall, Scott L ^a,d   Chapman, Wendy W ^e   Harkema, Henk ^f   Haug, Peter J ^a,b  

^a UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^b Center for Informatics Research   (United States)

^c University of Maryland ^*  (United States)

^d VETERANS AFFAIRS MEDICAL CENTER   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f NUANCE COMMUNICATIONS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ADAPTATION; ARTICLE; NARRATIVE; NATURAL LANGUAGE PROCESSING; PART OF SPEECH TAGGING; PERFORMANCE; PROBABILITY;

CLINICAL NARRATIVES; DOMAIN ADAPTATION; NATURAL LANGUAGE PROCESSING; NLP; POS TAGGING;

LINGUISTICS; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; NARRATION; NATURAL LANGUAGE PROCESSING;

EID: 84882738067     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2012-001453     Document Type: Article

References (59)

1. Haug PJ, Christensen L, Gundersen M, et al. A natural language parsing system for encoding admitting diagnoses. Proceedings of AMIA Annual Fall Symposium; 1997:814-18.
2. Haug PJ, Koehler SB, Christensen LM, et al. Inventors; Probabilistic method for natural language processing and for encoding free-text data into a medical database by utilizing a Bayesian network to perform spell checking of words. US patent 6292771. Alexandria, VA: United States Patent and Trademark Office, 2001 Sept 18.
3. Jain NL, Friedman C. Identification of findings suspicious for breast cancer based on natural language processing of mammogram reports. Proceedings of AMIA Annual Fall Symposium; 1997:829-33.
4. Jain NL, Knirsch CA, Friedman C, et al. Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports. Proceedings of AMIA Annual Fall Symposium; 1996:542-6.
5. Jones B, Ferraro JP, Haug P, et al. Performance of a real-time electronic screening tool for pneumonia [abstract]. Am J Respir Crit Care Med 2012;185:A5136.
6. Vines C, Collingsridge D, Jones B, et al. Emergency department physician experience with a real time, electronic pneumonia decision support tool [abstract]. Acad Emerg Med 2012;19(Suppl. 1):S49.
7. Chang MW, Do Q, Roth D. Multilingual dependency parsing: a pipeline approach. In: Nicolov N, Bontcheva K, Angelova G, et al. Recent advances in natural language processing IV. Amsterdam: John Benjamins Publishing Co., 2007:55-78.
8. Finkel JR, Manning CD, Ng AY. Solving the problem of cascading errors: approximate Bayesian inference for linguistic annotation pipelines. Proceedings of EMNLP; 2006:618-26.
9. Chapman WW, Nadkarni PM, Hirschman L, et al. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc 2011;18:540-3.
10. UMLS Reference Manual. Specialist lexicon and lexical tools. Bethesda, MD: National Library of Medicine (US); http://www.ncbi.nlm.nih.gov/books/NBK9680/ (accessed 7 Jul 2012).
11. Campbell DA, Johnson SB. A transformational-based learner for dependency grammars in discharge summaries. Proceedings of ACL; 2002;3:37-44.
12. Brill E, Resnik P. A rule-based approach to prepositional phrase attachment disambiguation. Proceedings of the 15th Conference on Computational Linguistics; 1994;2:1198-204.
13. Florian R, Ngai G. Multidimensional transformation-based learning. Proceedings of the 2001 Workshop on Computational Natural Language Learning; 2001;7: 1-8.
14. Kim J, Schwarm SE, Ostendorf M. Detecting structural metadata with decision trees and transformation-based learning. Proceedings of HLT/NAACL; 2004:137-44.
15. Jurcicek F, Gašic M, Keizer S, et al. Transformation-based learning for semantic parsing. Proceedings of Interspeech; 2009:2719-22.
16. Meystre SM, Savova GK, Kipper-Schuler KC, et al. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008;35:128-44.
17. Friedman C, Kra P, Rzhetsky A. Two biomedical sublanguages: a description based on the theories of Zellig Harris. J Biomed Inform 2002;35:222-35.
18. Friedman C. Semantic text parsing for patient records. In: Chen H, Fuller S, Friedman C. Medical informatics: knowledge management and data mining in biomedicine. 1st edn. New York: Springer-Verlag, 2005:423-48.
19. Ceusters W, Buekens F, De Moor G, et al. The distinction between linguistic and conceptual semantics in medical terminology and its implication for NLP-based knowledge acquisition. Methods Inf Med 1998;37:327-33.
20. Campbell DA, Johnson SB. Comparing syntactic complexity in medical and non-medical corpora. Proceedings of AMIA Symposium; 2001:90-4.
21. Coden AR, Pakhomov SV, Ando RK, et al. Domain-specific language models and lexicons for tagging. J Biomed Inform 2005;38:422-30.
22. Manning C. Part-of-speech tagging from 97% to 100%: is it time for some linguistics? Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics; 2011:171-89.
23. Toutanova K, Manning CD. Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. Joint Sigdat Conference on EMNLP/VLC; 2000:63-70.
24. Toutanova K, Klein D, Manning CD, et al. Feature-rich part-of-speech tagging with a cyclic dependency network. Proceedings of of HLT-NAACL; 2003:252-9.
25. Shen L, Satta G, Joshi A. Guided learning for bidirectional sequence classification. Proceedings of ACL; 2007;45:760-7.
26. Søgaard A. Simple semi-supervised training of part-of-speech taggers. Proceedings of ACL; 2010:205-8.
27. Wikipedia: POS tagging (state of the art). http://aclweb.org/aclwiki/index.php?title=POS_Tagging_(State_of_the_art) (accessed 2 Apr 2012).
28. Marcus MP, Marcinkiewicz MA, Santorini B. Building a large annotated corpus of English: the Penn Treebank. Comput Linguist 1993;19:313-30.
29. Baldridge J, Morton T, Bierner G. OpenNLP part-of-speech tagger. http://opennlp.apache.org/ (accessed 2 Apr 2012).
30. Ratnaparkhi A. A maximum entropy model for part-of-speech tagging. Proceedings of EMNLP; 1996;1:133-42.
31. Rizzolo N, Roth D. Learning based java for rapid development of nlp systems. Proceedings of LREC; 2010:958-64.
32. Roth D, Zelenko D. Part of speech tagging using a network of linear separators. The 17th International Conference on Computational Linguistics; 1998;2:1136-42.
33. Littlestone N. Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Machine Learning 1988;2:285-318.
34. Littlestone N. Mistake bounds and logarithmic linear-threshold learning algorithms [PhD thesis]. Santa Cruz: University of California, 1990. Technical Report UCSC-CRL-89-11.
35. LingPipe 4.1.0. http://alias-i.com/lingpipe (accessed 2 Apr 2012).
36. Kim JD, Ohta T, Tateisi Y, et al. GENIA corpus-a semantically annotated corpus for bio-textmining. Bioinformatics 2003;19(Suppl. 1):i180-2.
37. Smith L, Rindflesch T, Wilbur WJ. MedPost: a part-of-speech tagger for bioMedical text. Bioinformatics 2004;20:2320-1.
38. Daumé H III, Marcu D. Learning as search optimization: approximate large margin methods for structured prediction. Proceedings of the 22nd international conference on Machine Learning; 2005:169-76.
39. Daumé H. Frustratingly easy domain adaptation. Proceedings of 45th Ann Meeting of the Assoc Computational Linguistics; 2007;45:256-63.
40. Brill E. Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput Linguist; 1995;21:543-65.
41. Pakhomov SV, Coden A, Chute CG. Developing a corpus of clinical notes manually annotated for part-of-speech. Int J Med Inform 2006;75:418-29.
42. Brants T. TnT: a statistical part-of-speech tagger. Proceedings of ANLP; 2000:224-31.
43. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
44. Fan J, Prasad R, Yabut RM, et al. Part-of-speech tagging for clinical text: wall or bridge between institutions? AMIA Annual Symposium Proceedings 2011:382-91.
45. Liu K, Chapman W, Hwa R, et al. Heuristic sample selection to minimize reference standard training set for a part-of-speech tagger. J Am Med Inform Assoc 2007;14:641-50.
46. Hwa R. Sample selection for statistical parsing. Comput Linguis 2004;30:253-76.
47. Blitzer J, McDonald R, Pereira F. Domain adaptation with structural correspondence learning. Proceedings of EMNLP; 2006:120-8.
48. Santorini B. Part-of-speech tagging guidelines for the Penn Treebank project (3rd revision), Technical Report MS-CIS-90-47. University of Pennsylvania; 1990.
49. Browne AC, McCray AT, Srinivasan S. The specialist lexicon. Bethesda, MD: Lister Hill National Center for Biomedical Communications, National Library of Medicine; 2000:18-21, NLM Technical Report NLM-LHC-93-01.
50. Fleiss JL. Measuring nominal scale agreement among many raters. Psychol Bull 1971;76:378-82.
51. Biomedical Language Understanding (BLU) Lab-University of Pittsburgh. Pittsburgh, PA; http://www.dbmi.pitt.edu/blulab/index.html (accessed 7 Jul 2012).
52. Yount RJ, Vries JK, Councill CD. The Medical Archival System: an information retrieval system based on distributed parallel processing. Inf Process Manage 1991;27:379-89.
53. Wood JM. Understanding and computing Cohen's kappa: a tutorial. WebPsychEmpiricist http://wpeinfo/vault/wood07/Wood07pdf (accessed 2 Apr 2012).
54. World of computing. Articles on natural language processing: transformation based learning. http://language.worldofcomputing.net/pos-tagging/transformation-basedlearning.html# (accessed 7 Jul 2012).
55. Brill E. Some advances in transformation-based part of speech tagging. Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94); 1994:722-7.
56. Kneser R, Ney H. Improved backing-off for m-gram language modeling. IEEE International Conference on Acoustics, Speech, and Signal Processing; 1995;1:181-4.
57. Kay M. A life of language. Computat Linguist 2005;31:425-38.
58. Ngai G, Florian R. Transformation-based learning in the fast lane. Proceedings of NAACL; 2001:40-7.
59. Carberry S, Vijay-Shanker K, Wilson A, et al. Randomized rule selection in transformation-based learning: a comparative study. Nat Lang Eng 2001;7:99-116.