Word sense disambiguation across two domains: Biomedical literature and clinical notes

Journal of Biomedical Informatics

Volumn 41, Issue 6, 2008, Pages 1088-1100

  Savova, Guergana K ^a   Coden, Anni R ^b   Sominsky, Igor L ^b   Johnson, Rie ^c   Ogren, Philip V ^a   Groen, Piet C de ^a   Chute, Christopher G ^a  

^a Mayo Clinic   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c RJ Research Consulting   (United States)

Author keywords

Artificial intelligence; Biomedical natural language processing; Information extraction; Machine learning; Natural language processing; Word sense disambiguation

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; KETONES; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINGUISTICS; ROBOT LEARNING;

BIOMEDICAL NATURAL LANGUAGE PROCESSING; INFORMATION EXTRACTION; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING; WORD SENSE DISAMBIGUATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

ALGORITHM; AMBIGUITY; ARTICLE; ARTIFICIAL INTELLIGENCE; INFORMATION PROCESSING; MACHINE LEARNING; MEDICAL LITERATURE; NATURAL LANGUAGE PROCESSING; PRIORITY JOURNAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; INFORMATION SERVICES; LANGUAGE;

EID: 55549101625     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2008.02.003     Document Type: Article

References (28)

1. Shatkay H., and Feldman R. Mining the biomedical literature in the genomic era: overview. J Comput Biol 10 (2003) 821-855
2. Schuler G., Epstein J., Ohkawa H., and Kans J. Entrez: molecular biology database and retrieval system. Methods Enzymol 266 (1996) 141-162
3. Krallinger M., and Valencia A. Text mining and information retrieval services for molecular biology. Genome Biol 6 (2005) 224
4. Aronson A.R., Bodenreider O., Chang H.F., et al. The NLM indexing initiative. Proc AMIA Symp (2000)
5. Friedman C. Towards a comprehensive medical language processing system: methods and issues. Proc AMIA Symp (1997)
6. Humphreys B., Lindberg D., Schoolman H., and Barnett G. The unified medical language system: an informatics research collaboration. J Am Med Assoc 5 (1998) 1-11
7. Weeber M., Mork J.G., and Aronson A.R. Developing a test collection for biomedical word sense disambiguation. Proc AMIA Symp (2001)
8. Liu H., Teller V., and Friedman C. A multi-aspect comparison study of supervised word sense disambiguation. JAMIA 11 4 (2004)
9. Leroy G., and Rindflesch T. Using symbolic knowledge in the UMLS to disambiguate words in small datasets with a Naïve Bayes classifier. Proc Medinfo (2004)
10. Xu H., Markatou M., Dimova R., Liu H., and Friedman C. Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues. BMC Bioinformatics 7 (2006) 334
11. Sehgal A.K., Srinivasan P., and Bodenreider O. Gene terms and English words: an ambiguous mix. SIGIR'04 Workshop on Search and Discovery in Bioinformatics (2004)
12. In: Agirre E., and Edmonds P. (Eds). Word sense disambiguation: algorithms and applications (2006), Springer
13. Schutze H. Automatic word sense discrimination. Comput Linguist 24 (1998) 97-123
14. Witten I.H., and Eibe F. Data mining: practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufmann, San Francisco
15. Schuemie M.J., Kors J.A., and Mons B. Word sense disambiguation in the biomedical domain: an overview. J Comput Biol 12 (2005) 554-565
16. Leroy G., and Rindflesch T. Effects of information and machine learning algorithms on word sense disambiguation with small datasets. Int J Med Inform 74 (2005) 573-585
17. Humphrey S.M., Rogers W.J., Kilicoglu H., Demner-Fushman D., and Rindflesch T.C. Word sense disambiguation by selecting the best semantic type based on Journal Descriptor Indexing: preliminary experiment. J Am Soc Inform Sci Technol 57 (2006) 96-113
18. Pakhomov S.V., Pedersen T., and Chute C.G. Abbreviation and acronym disambiguation in clinical discourse. Proc AMIA Symp (2005) 589-593
19. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas 20 (1960) 37-46
20. Carletta J. Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22 2 (1996) 249-254
21. Poesio M., and Vieira R. A corpus-based investigation of definite description use. Comput Linguist 24 2 (1998) 183-216
22. Chklovski T., and Mihalcea R. Exploiting agreement and disagreement of human annotations for word sense disambiguation. RANLP (2003)
23. Ando RK. Applying Alternating Structure Optimization to Word Sense Disambiguation. In: Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL), 2006.
24. Lee, Yoong Keok, Ng, Hwee Tou. An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In: Proceedings of EMNLP-2002, 2002.
25. Zhang T. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: ICML 2004, p. 919-26.
26. Coden A., Pakhomov S., Ando R., and Chute C. Domain-specific language models and lexicons for tagging. JBI (2005)
27. Cohen P. Empirical methods for artificial intelligence (1995), The MIT Press, Cambridge, Massachusetts
28. Clopper C.J., and Pearson E.S. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrica 26 (1934) 404-413