Biomedical word sense disambiguation with ontologies and metadata: Automation meets accuracy

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Alexopoulou, Dimitra ^a   Andreopoulos, Bill ^a   Dietze, Heiko ^a   Doms, Andreas ^a   Gandon, Fabien ^b   Hakenberg, Jörg ^a   Khelif, Khaled ^b   Schroeder, Michael ^a   Wächter, Thomas ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^b INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED METHODS; CLASSICAL APPROACH; GENE ONTOLOGY; ONTOLOGY TERMS; SEMANTIC SIMILARITY; SHORTEST PATH; TRAINING CORPUS; WORD SENSE DISAMBIGUATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

METADATA;

ACCURACY; ARTICLE; AUTOMATION; BIOMEDICINE; CLASSIFICATION; CONTROLLED STUDY; DATA ANALYSIS; DATA BASE; INTERMETHOD COMPARISON; MEDICAL DOCUMENTATION; NOMENCLATURE; QUALITY CONTROL; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGY; INFORMATION RETRIEVAL; LINGUISTICS; MEDICAL INFORMATICS; MEDICAL INFORMATION SYSTEM; MESH HEADING; METHODOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; INFORMATION STORAGE AND RETRIEVAL; MEDICAL INFORMATICS; MEDICAL SUBJECT HEADINGS; PATTERN RECOGNITION, AUTOMATED; UNIFIED MEDICAL LANGUAGE SYSTEM; VOCABULARY, CONTROLLED;

EID: 63849148879     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-28     Document Type: Article

References (51)

1. Schuemie MJ Kors JA Mons B Word sense disambiguation in the biomedical domain: An overview J Comput Biol 2005, 12(5):554-565. 15952878
2. Gale WA Church KW Yarowsky D One sense per discourse HLT '91: Proceedings of the workshop on Speech and Natural Language Morristown, NJ, USA: Association for Computational Linguistics 1992:233-237.
3. Yarowsky D One sense per collocation HLT '93: Proceedings of the workshop on Human Language Technology Morristown, NJ, USA: Association for Computational Linguistics 1993:266-271.
4. Weeber M Mork JG Aronson AR Developing a Test Collection for Biomedical Word Sense Disambiguation Proc AMIA Symp 2001:746-750. 2243574 11825285
5. Automatic extraction of acronym-meaning pairs from MEDLINE databases Stud Health Technol Inform 2001, 84(Pt 1):371-375. 11604766
6. Biocreative set http://www.mitre.org/public/biocreative/
7. Edmonds P Agirre E Word Sense Disambiguation: Algorithms And Applications Springer Verlag; 2006.
8. Hatzivassiloglou V Duboue PA Rzhetsky A Disambiguating proteins, genes, and RNA in text: A machine learning approach Bioinformatics 2001, 17(suppl-1):S97-106. 11472998
9. Liu H Teller V Friedman C A Multi-aspect Comparison Study of Supervised Word Sense Disambiguation J Am Med Inform Assoc 2004, 11(4):320-331. 436083 15064284
10. Gaudan S Kirsch H Rebholz-Schuhmann D Resolving abbreviations to their senses in Medline Bioinformatics 2005, 21(18):3658-3664. 16037121
11. Pahikkala T Ginter F Boberg J Järvnen J Salakoski T Contextual weighting for Support Vector Machines in literature mining: An application to gene versus protein name disambiguation BMC Bioinformatics 2005, 6(157):. 1180820 15972097
12. Schijvenaars B Mons B Weeber M Schuemie M van Mulligen E Wain H Kors J Thesaurus-based disambiguation of gene symbols BMC Bioinformatics 2005, 6:149. 1183190 15958172
13. Humphrey SM Rogers WJ Kilicoglu H Demner-Fushman D Rindflesch TC Word sense disambiguation by selecting the best semantic type based on Journal Descriptor Indexing: Preliminary experiment Journal of the American Society for Information Science and Technology 2006, 57:96-113.
14. Hakenberg J Plake C Royer L Strobelt H Leser U Schroeder M Gene mention normalization and interaction extraction with context models and sentence motifs Genome Biol 2008, 9(Suppl 2):S14. 18834492 2559985
15. Farkas R The strength of co-authorship in gene name disambiguation BMC Bioinformatics 2008, 9:69. 18230174 2262057
16. Bodenreider O The Unified Medical Language System (UMLS): Integrating biomedical terminology Nucleic Acids Research 2004, 32(D267-70):. 308795 14681409
17. Nelson S Johnston D Humphreys B Relationships in the organization of knowledge New York: Kluwer Academic Publishers 2001 chap. Relationships in Medical Subject Headings:171-184.
18. Ashburner M Ball C Blake J Botstein D Butler H Cherry J Davis A Dolinski K Dwight S Eppig J Harris M Hill D Issel-Tarver L Kasarskis A Lewis S Matese J Richardson J Ringwald M Rubin G Sherlock G Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium Nat Genet 2000, 25:25-9. 10802651
19. Schütze H Pedersen J Information retrieval based on word senses Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval: 1995 Las Vegas, NV 1995:161-175.
20. Schütze H Automatic word sense discrimination Comput Linguist 1998, 24:97-123.
21. Pedersen T Bruce R Knowledge Lean Word Sense Disambiguation Proceedings of the Fifteenth National Conference on Artificial Intelligence Madison, WI 1998:800-805.
22. Purandare A Pedersen T Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces Proceedings of CoNLL-2004 Boston, MA, USA 2004:41-48.
23. Yarowsky D Unsupervised Word Sense Disambiguation Rivaling Supervised Methods Meeting of the Association for Computational Linguistics 1995:189-196.
24. Dorow B Widdows D Discovering corpus-specific word senses EACL '03: Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics Morristown, NJ, USA: Association for Computational Linguistics 2003:79-82.
25. Mihalcea R Co-training and Self-training for Word Sense Disambiguation Proceedings of CoNLL-2004 Boston, MA, USA 2004:33-40.
26. Widdows D Peters S Cederberg S Chan CK Steffen D Buitelaar P Unsupervised Monolingual and Bilingual Word-Sense Disambiguation of Medical Documents using UMLS ACL Workshop on Natural Language Processing in Biomedicine Sapporo, Japan; 2003.
27. Leroy G Rindflesch T Effects of information and machine learning algorithms on word sense disambiguation with small datasets Int J Med Inform 2005, 74(7-8):573-85. 15897005
28. Fellbaum C WordNet An Electronic Lexical Database USA: MIT Press; 1998.
29. Rada R Mili H Bicknell E Blettner M Development and application of a metric on semantic nets Systems, Man and Cybernetics, IEEE Transactions on 1989, 19:17-30.
30. Sussna M Word sense disambiguation for free-text indexing using a massive semantic network CIKM '93: Proceedings of the second international conference on Information and knowledge management New York, NY, USA: ACM Press 1993:67-74.
31. Resnik P Using Information Content to Evaluate Semantic Similarity in a Taxonomy Proceedings of the 14th International Joint Conference on Artificial Intelligence 1995.
32. Lin D An information-theoretic definition of similarity Proc 15th International Conf on Machine Learning Morgan Kaufmann, San Francisco, CA 1998:296-304.
33. Lord PW Stevens RD Brass A Goble CA Investigating semantic similarity measures across the Gene Ontology: The relationship between sequence and annotation Bioinformatics 2003, 19(10):1275-1283. 12835272
34. Azuaje F Wang H Bodenreider O Ontology-driven similarity approaches to supporting gene functional assessment Proceedings of the ISMB'2005 SIG meeting on Bio-ontologies 2005:9-10.
35. Schlicker A Domingues F Rahnenfuhrer J Lengauer T A new measure for functional similarity of gene products based on Gene Ontology BMC Bioinformatics 2006, 7:302. 1559652 16776819
36. del Pozo A Pazos F Valencia A Defining functional distances over Gene Ontology BMC Bioinformatics 2008, 9(50):. 2375122 18221506
37. Andreopoulos B Alexopoulou D Schroeder M Word Sense Disambiguation in Biomedical Ontologies with Term Co-occurrence Analysis and Document Clustering Int J Data Min Bioinform 2008, 2(3):193-215. 19024494
38. Camon E Magrane M Barrell D Lee V Dimmer E Maslen J Binns D Harte N Lopez R Apweiler R The Gene Ontology Annotation (GOA) Database: Sharing knowledge in Uniprot with Gene Ontology Nucleic Acids Res 2004, 32(Database):D262-D266. 308756 14681408
39. Bodenreider O Stevens R Bio-ontologies: Current trends and future directions Brief Bioinform 2006, 7(3):256-74. 1847325 16899495
40. Khelif K Dieng-Kuntz R Barbry P An ontology-based approach to support text mining and information retrieval in the biological domain Special Issue on Ontologies and their Applications of the Journal of Universal Computer Science (JUCS) 2007, 13(12):1881-1907.
41. Klinkenberg R Joachims T Detecting Concept Drift with Support Vector Machines Proc 17th International Conference on Machine Learning (ICML) 2000.
42. Berger AL Pietra VJD Pietra SAD A maximum entropy approach to natural language processing Comput Linguist 1996, 22:39-71.
43. Pietra SD Pietra VJD Lafferty JD Inducing Features of Random Fields IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(4):380-393.
44. Porter MF An algorithm for suffix stripping San Francisco, CA, USA: Morgan Kaufmann Publishers Inc; 1997.
45. Maximum Entropy implementation http://sourceforge.net/projects/maxent/
46. Doms A Schroeder M GoPubMed: Exploring PubMed with the Gene Ontology Nucl Acids Res 2005, 33:W783-786. 1160231 15980585
47. Alexopoulou D Wächter T Pickersgill L Eyre C Schroeder M Terminologies for text-mining; an experiment in the lipoprotein metabolism domain BMC Bioinformatics 2008, 9(Suppl 4):S2. 2367629 18460175
48. Xu H Markatou M Dimova R Liu H Friedman C Machine learning and word sense disambiguation in the biomedical domain: Design and evaluation issues BMC Bioinformatics 2006, 7:334. 1550263 16822321
49. Ginter F Boberg J Järvinen J Salakoski T New Techniques for Disambiguation in Natural Language and Their Application to Biological Text J Mach Learn Res 2004, 5:605-621.
50. Liu H Johnson SB Friedman C Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS J Am Med Inform Assoc 2002, 9(6):621-636. 349379 12386113
51. Pedersen T Bruce R Distinguishing Word Senses in Untagged Text Proceedings of the Second Conference on Empirical Methods in Natural Language Processing Providence, RI 1997:197-207.