TaggerOne: Joint named entity recognition and normalization with semi-Markov Models

Bioinformatics

Volumn 32, Issue 18, 2016, Pages 2839-2846

  Leaman, Robert ^a   Lu, Zhiyong ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED PATTERN RECOGNITION; BIOLOGY; DATA MINING; DISEASES; FORECASTING; INFORMATION RETRIEVAL; KNOWLEDGE BASE; MACHINE LEARNING; NOMENCLATURE; SEMANTICS; SOFTWARE;

COMPUTATIONAL BIOLOGY; DATA MINING; DISEASE; FORECASTING; INFORMATION STORAGE AND RETRIEVAL; KNOWLEDGE BASES; MACHINE LEARNING; PATTERN RECOGNITION, AUTOMATED; SEMANTICS; SOFTWARE; TERMINOLOGY AS TOPIC;

EID: 84992220733     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw343     Document Type: Article

References (48)

1. Altun, Y. et al. (2007) Support vector machine learning for independent and structured output spaces. In: Bakir, G. et al. (eds) Predicting Structured Data. The MIT Press, Cambridge, Massachusetts, USA.
2. Bai, B. et al. (2010) Learning to rank with (a lot of) word features. Inf. Retrieval, 13, 291-314.
3. Blair, D.R. et al. (2014) Quantifying the impact and extent of undocumented biomedical synonymy. PLoS Comput. Biol., 10, e1003799.
4. Campos, D. et al. (2013) A modular framework for biomedical concept recognition. BMC Bioinformatics, 14, 281.
5. Chowdhury, F.M. and Lavelli, A. (2010) Disease mention recognition with specific features. Bio NLP Workshop. Uppsala, Sweden, pp. 83-90.
6. Cohen, W.W. and Sarawagi, S. (2004) Exploiting Dictionaries in Named Entity Extraction: Combining Semi-Markov Extractions Processes and Data Integration Methods. 10th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. ACM, Seattle, Washington, USA, pp. 89-98.
7. Crammer, K. and Singer, Y. (2001) On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res., 2, 265-292.
8. Crammer, K. and Singer, Y. (2003) Ultraconservative online algorithms for multiclass problems. J. Mach. Learn. Res., 3, 951-991.
9. D'Souza, J. and Ng, V. (2015) Sieve-Based Entity Linking for the Biomedical Domain. In: 53rd ACL and 7th IJCNLP. Beijing, China, pp. 297-302.
10. Doǧan, R.I. et al. (2014) NCBI disease corpus: A resource for disease name recognition and concept normalization. J. Biomed. Inf., 47, 1-10.
11. Durrett, G. and Klein, D. (2014) A joint model for entity analysis: coreference, typing and linking. Trans. Assoc. Comput. Linguist., 2, 477-490.
12. Eltyeb, S. and Salim, N. (2014) Chemical named entities recognition: a review on approaches and applications. J. Cheminf., 6, 17.
13. Finkel, J.R. and Manning, C.D. (2009) Joint Parsing and Named Entity Recognition. NAACL/HLT. Boulder, Colorado, pp. 326-334.
14. Finkel, J.R. and Manning, C.D. (2010) Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data. 48th ACL. Uppsala, Sweden, pp. 720-728.
15. Hartman, D. (1991) How effective is suffixing? J. Am. Soc. Inf. Sci. Technol., 42, 7-15.
16. Hirschman, L. et al. (2005) Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics, 6, S1.
17. Jessop, D.M. et al. (2011) OSCAR4: a flexible architecture for chemical textmining. J. Cheminf., 3, 41.
18. Jimeno, A. et al. (2008) Assessment of disease named entity recognition on a corpus of annotated sentences. BMC Bioinformatics, 9, S3.
19. Kang, N. et al. (2012) Using rule-based natural language processing to improve disease normalization in biomedical text. J. Am. Med. Inf. Assoc., 20, 876-881.
20. Kim, J.D. et al. (2009) Overview of BioNLP'09 shared task on event extraction. Bio NLP Workshop, pp. 1-9.
21. Klinger, R. et al. (2008) Detection of IUPAC and IUPAC-like chemical names. Bioinformatics, 24, i268-i276.
22. Kolarik, C. et al. (2008) Chemical names: terminological resources and corpora annotation. LREC Workshop on Building and Evaluating Resources for Bbiomedical Text Mining.
23. Krallinger, M. et al. (2015a) CHEMDNER: The drugs and chemical names extraction challenge. J. Cheminf., 7, S1.
24. Krallinger, M. et al. (2015b) Overview of the CHEMDNER Patents Task. Fifth Bio Creative Challenge Evaluation Workshop. Seville, Spain, pp. 63-75.
25. Le, H.-Q. et al. (2015) The UET-CAM System in the BioCreAtIvE V CDR Task. Bio Creative Workshop. Seville, Spain, pp. 208-213.
26. Leaman, R. et al. (2013) DNorm: Disease name normalization with pairwise learning-to-rank. Bioinformatics, 29, 2909-2917.
27. Leaman, R. and Gonzalez, G. (2008) BANNER: an executable survey of advances in biomedical named entity recognition. Pac. Symp. Biocomput., 652-663.
28. Leaman, R. et al. (2009) Enabling recognition of diseases in biomedical text with machine learning: corpus and benchmark. Proc Symp on Languages in Biology and Medicine, 13, pp. 82-89.
29. Leaman, R. et al. (2015a) Challenges in clinical natural language processing for automated disorder normalization. J. Biomed. Inf, 57, pp. 28-37.
30. Leaman, R. et al. (2015b) tmChem: a high performance approach for chemical named entity recognition and normalization. J. Cheminf., 7, S3.
31. Lee, H.C. et al. (2015) An Enhanced CRF-Based System for Disease Name Entity Recognition and Normalization on BioCreative V DNER Task. Proc Bio Creative Workshop. Sevilla, Spain, pp. 226-233.
32. Li, J. et al. (2015) Annotating chemicals, diseases and their interactions in biomedical literature. Proc Bio Creative Workshop. Seville, Spain, pp. 173-182.
33. Morgan, A.A. et al. (2008) Overview of BioCreative II gene normalization. Genome Biol., 9, S3.
34. Okanohara, D. et al. (2006) Improving the scalability of semi-markov conditional random fields for named entity recognition. 21st Int Conf on Comp Ling and 44th ACL. Association for Computational Linguistics, pp. 465-472.
35. Porter, M.F. (1980) An algorithm for suffix stripping. Program, 14, 130-137.
36. Pradhan, S. et al. (2015) Evaluating the state of the art in disorder recognition and normalization of the clinical narrative. J. Am. Med. Inf. Assoc., 22, 143-154.
37. Pyysalo, S. and Ananiadou, S. (2014) Anatomical entity mention recognition at literature scale. Bioinformatics, 30, 868-875.
38. Rebholz-Schuhmann, D. et al. (2010) CALBC silver standard corpus. J. Bioinf. Comput. Biol., 8, 163-179.
39. Rocktaschel, T. et al. (2012) Chem Spot: a hybrid system for chemical named entity recognition. Bioinformatics, 28, 1633-1640.
40. Sohn, S. et al. (2008) Abbreviation definition identification based on automatic precision estimates. BMC Bioinformatics, 9, 402.
41. Song, M. et al. (2015) PKDE4J: Entity and relation extraction for public knowledge discovery. J. Biomed. Inf, 57, 320-332.
42. Taskar, B. et al. (2004) Max-margin Markov networks. In: Thrun, S. et al. (eds) Adv Neural Inf Process Syst. MIT Press, Cambridge, Massachusetts, USA.
43. Tsuruoka, Y. et al. (2007) Learning string similarity measures for gene/protein name dictionary look-up using logistic regression. Bioinformatics, 23, 2768-2774.
44. Usami, Y. et al. (2011) Automatic acquisition of huge training data for biomedical named entity recognition. Bio NLP Workshop. Portland, Oregon, pp. 65-73.
45. Vazquez, M. et al. (2011) Text mining for drugs and chemical compounds: methods, tools and applications. Mol. Inf., 30, 506-519.
46. Wei, C.H. et al. (2015a) GNorm Plus: an integrative approach for tagging genes, gene families, and protein domains. Bio Med. Res. Int., 2015, 7.
47. Wei, C.H. et al. (2015b) Sim Concept: a hybrid approach for simplifying composite named entities in biomedical text. IEEE J. Biomed. Health Inf., 19, 1385-1391.
48. Wei, C.H. et al. (2015c) Overview of the BioCreative V Chemical Disease Relation (CDR) Task. Bio Creative Workshop. pp. 154-166.