Gimli: Open source and high-performance biomedical name recognition

BMC Bioinformatics

Volumn 14, Issue , 2013, Pages

  Campos, David ^a   Matos, Sérgio ^a   Oliveira, José Luís ^a  

^a UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC RECOGNITION; BIOMEDICAL INFORMATION EXTRACTIONS; CONFIGURATION FILES; NAME RECOGNITION; NAMED-ENTITY RECOGNITION; OPTIMIZED MODELS; SYSTEM CHARACTERISTICS; UNDERLYING SYSTEMS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

DATA MINING;

DNA; PROTEIN; RNA;

ARTICLE; CELL LINE; COMPUTER PROGRAM; DATA MINING; GENE; HUMAN; LINGUISTICS; METHODOLOGY;

CELL LINE; DATA MINING; DNA; GENES; HUMANS; PROTEINS; RNA; SOFTWARE; VOCABULARY, CONTROLLED;

EID: 84873744177     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-54     Document Type: Article

References (36)

1. Tsuruoka Y, Tateishi Y, Kim J, Ohta T, McNaught J, Ananiadou S, Tsujii J. Developing a robust part-of-speech tagger for biomedical text. Advances in informatics 2005, 3746:382-392.
2. Leaman R, Gonzalez G. BANNER: An executable survey of advances in biomedical named entity recognition. Pacific, Symposium on Biocomputing, Volume 13 2008, 652-663. Big Island, Hawaii: .
3. Settles B. ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics 2005, 21(14):3191. 10.1093/bioinformatics/bti475, 15860559.
4. Song Y, Kim E, Lee G, Yi B. POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004, 100-103. Stroudsburg, PA, USA: Association for Computational Linguistics.
5. Finkel J, Dingare S, Nguyen H, Nissim M, Manning C, Sinclair G. Exploiting context for biomedical entity recognition: From syntax to the web. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004, 88-91. Stroudsburg, PA, USA: Association for Computational Linguistics.
6. Cho HC. NERsuite: A Named Entity Recognition toolkit. Tsujii Laboratory, Department of Information Science, University of Tokyo, Tokyo, Japan 2010, [http://nersuite.nlplab.org].
7. Ando R. BioCreative II gene mention tagging system at IBM Watson. Proceedings of the Second BioCreative Challenge Evaluation Workshop 2007, 101-103. Madrid, Spain: .
8. Zhou G, Zhang J, Su J, Shen D, Tan C. Recognizing names in biomedical texts: a machine learning approach. Bioinformatics 2004, 20(7):1178-90. 10.1093/bioinformatics/bth060, 14871877.
9. Tsai R, Sung C, Dai H, Hung H, Sung T, Hsu W. NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition. BMC bioinformatics 2006, 7(Suppl 5):S11. 10.1186/1471-2105-7-S5-S11, 1764467, 17254295.
10. Hsu C, Chang Y, Kuo C, Lin Y, Huang H, Chung I. Integrating high dimensional bi-directional parsing models for gene mention tagging. Bioinformatics 2008, 24(13):i286. 10.1093/bioinformatics/btn183, 2718659, 18586726.
11. McCallum AK. MALLET: A Machine Learning for Language Toolkit. Amherst, MA, USA 2002, [http://mallet.cs.umass.edu].
12. Sagae K. Dependency parsing and domain adaptation with, LR models and parser ensembles. Proceedings of the Eleventh Conference on Computational Natural Language Learning 2007, 1044-1050. Prague, Czech Republic: .
13. Liu H, Hu ZZ, Zhang J, Wu CH. BioThesaurus: a web-based thesaurus of protein and gene names. Bioinformatics 2006, 22:103-105. 10.1093/bioinformatics/bti749, 16267085.
14. Sasaki Y, Montemagni S, Pezik P, Rebholz-Schuhmann D, McNaught J, Ananiadou S. Biolexicon: A lexical resource for the biology domain. Proceedings of the, Third International Symposium on Semantic Mining in Biomedicine (SMBM 2008), Volume 3 2008, 109-116. Jena, Germany: .
15. Tanabe L, Xie N, Thom L, Matten W, Wilbur W. GENETAG: a tagged corpus for gene/protein named entity recognition. BMC bioinformatics 2005, 6(Suppl 1):S3. 10.1186/1471-2105-6-S1-S3, 1866382, 16351752.
16. Smith L, Tanabe L, Ando R, Kuo C, Chung I, Hsu C, Lin Y, Klinger R, Friedrich C, Ganchev K. Overview of BioCreative II gene mention recognition. Genome Biology 2008, 9(Suppl 2):S2. 10.1186/gb-2008-9-s2-s2, 2559986, 18834493.
17. Kim J, Ohta T, Tsuruoka Y, Tateisi Y, Collier N. Introduction to the bio-entity recognition task at JNLPBA. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004, 70-75. Stroudsburg, PA, USA: Association for Computational Linguistics.
18. Kim J, Ohta T, Tateisi Y, Tsujii J. GENIA corpus-a semantically annotated corpus for bio-textmining. Bioinformatics 2003, 19:180-182.
19. Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticae Investigationes 2007, 30:3-26.
20. Zhou G, Shen D, Zhang J, Su J, Tan S. Recognition of protein/gene names from text using an ensemble of classifiers. BMC bioinformatics 2005, 6(Suppl 1):S7. 10.1186/1471-2105-6-S1-S7, 1866378, 16351756.
21. Collins M. Ranking algorithms for named-entity extraction: Boosting and the voted perceptron. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics 2002, 489-496. Philadelphia, PA, USA: Association for Computational Linguistics.
22. Vlachos A. Tackling the, BioCreative2 gene mention task with conditional random fields and syntactic parsing. Proceedings of the Second BioCreative Challenge Evaluation Workshop; 23 to 25 April 2007 2007, 85-87. Madrid, Spain: .
23. Schuemie M, Mons B, Weeber M, Kors J. Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification. Journal of Biomedical Informatics 2007, 40(3):316-324. 10.1016/j.jbi.2006.09.002, 17079192.
24. Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001) 2001, 282-289. Williamstown, MA, USA: .
25. Wallach H. Conditional random fields: An introduction. Tech. rep., University of Pennsylvania, Philadelphia, PA, USA 2004.
26. Keerthi S, Sundararajan S. CRF versus SVM-struct for sequence labeling. Tech. rep., Yahoo Research 2007.
27. Lee C, Jang M. Fast training of structured SVM using fixed-threshold sequential minimal optimization. ETRI journal 2009, 31(2):121-128.
28. Hoefel G, Elkan C. Learning a two-stage SVM/CRF sequence classifier. Proceedings of the 17th ACM conference on Information and knowledge management, CIKM '08 2008, 271-278. New York, NY, USA: ACM.
29. Sarawagi S, Cohen W. Semi-markov conditional random fields for information extraction. Advances in Neural Information Processing Systems 2004, 17:1185-1192.
30. Campos D, Matos S, Lewin I, Oliveira J, Rebholz-Schuhmann D. Harmonization of gene/protein annotations: towards a gold standard MEDLINE. Bioinformatics 2012, 28(9):1253-1261. 10.1093/bioinformatics/bts125, 22419783.
31. Schwartz A, Hearst M. A simple algorithm for identifying abbreviation definitions in biomedical text. Pacific Symposium on Biocomputing, Volume 8 2003, 451-462. Lihue, Hawaii: .
32. Kuo C, Chang Y, Huang H, Lin K, Yang B, Lin Y, Hsu C, Chung I. Rich feature set, unification of bidirectional parsing and dictionary filtering for high F-score gene mention tagging. Proceedings of the Second BioCreative Challenge Evaluation Workshop 2007, 105-107. Madrid, Spain: .
33. Neves M, Chagoyen M, Carazo J, Pascual-Montano A. CBR-Tagger: a case-based reasoning approach to the gene/protein mention problem. Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing 2008, 108-109. Stroudsburg, PA, USA: Association for Computational Linguistics.
34. Alias-i I. LingPipe. Alias-i, Inc., Brooklyn, NY, USA 2011, [http://alias-i.com/lingpipe/index.html].
35. McCallum A. Efficiently inducing features of conditional random fields. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence 2003, 403-410. Acapulco, Mexico: Morgan Kaufmann Publishers Inc..
36. Mann G, McCallum A. Generalized expectation criteria for semi-supervised learning of conditional random fields. Proc. ACL 2008, 870-878. Columbus, Ohio, USA: .