Biomedical named entity recognition using generalized expectation criteria

International Journal of Machine Learning and Cybernetics

Volumn 2, Issue 4, 2011, Pages 235-243

  Yao, Lin ^a   Sun, Chengjie ^a   Wu, Yan ^a   Wang, Xiaolong ^a   Wang, Xuan ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Biomedical named entity recognition; Conditional random field; General expectation; Latent Dirichlet allocation; Semi supervised learning

Indexed keywords

BIOMEDICAL NAMED ENTITY RECOGNITION; CONDITIONAL RANDOM FIELD; GENERAL EXPECTATION; LATENT DIRICHLET ALLOCATION; SEMI-SUPERVISED LEARNING;

LEARNING ALGORITHMS; STATISTICS; SUPERVISED LEARNING;

RANDOM PROCESSES;

EID: 81255185846     PISSN: 18688071     EISSN: 1868808X     Source Type: Journal    
DOI: 10.1007/s13042-011-0022-3     Document Type: Article

References (45)

1. Dai H et al (2010) New challenges for biological text-mining in the next decade. J Comput Sci Technol 25(1): 169-179.
2. Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Linguisticae Investigationes 30: 3-26.
3. Hu Q et al (2010) An efficient gene selection technique for cancer recognition based on neighborhood mutual information. Int J Mach Learn Cybern 1-12.
4. Kuncheva LI (2010) Full-class set classification using the Hungarian algorithm. Int J Mach Learn Cybern 1(1-4): 53-61.
5. Krallinger M et al (2008) Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge. Genome Biol 9(Suppl 2): 1.
6. Dai H et al (2008) BIOSMILE web search: a web application for annotating biomedical entities and relations. Nucl Acids Res 36(Web Server issue): W390.
7. Rebholz-Schuhmann D (2008) Text processing through web services: calling Whatizit. Bioinformatics 24(2): 296-298.
8. Si L, Kanungo T, Huang X (2005) Boosting performance of bio-entity recognition by combining results from multiple systems. In: Proceedings of the 5th international workshop on Bioinformatics, ACM, pp 76-83.
9. Vlachos A (2007) Evaluating and combining biomedical named entity recognition systems, In: BioNLP 2007: biological, translational, and clinical language processing, pp 199-206.
10. Saha SK, Sarkar S, Mitra PP (2009) Feature selection techniques for maximum entropy based biomedical named entity recognition. J Biomed Inform 42(5): 905-911.
11. Lin YF et al (2004) A maximum entropy approach to biomedical named entity recognition. In: Proceedings of the 4th ACM SIGKDD Workshop on Data Mining in Bioinformatics, Citeseer, pp 56-61.
12. Lee KJ, Hwang YS, Rim HC (2003) Two-phase biomedical NE recognition based on SVMs. In: Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine. Association for Computational Linguistics, pp 33-40.
13. Li L, Zhou R, Huang D (2009) Two-phase biomedical named entity recognition using CRFs. Comput Biol Chem 33(4): 334-338.
14. Zhou G, Su J (2004) Exploring deep knowledge resources in biomedical name recognition in the joint workshop on natural language processing in biomedicine and its applications. In: Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA 2004), pp. 96-99.
15. Lee K et al (2004) Biomedical named entity recognition using two-phase model based on SVMs. J Biomed Inform 37(6): 436-447.
16. Nigam K et al (2000) Text classification from labelled and unlabelled documents using EM. Mach Learn 103-134.
17. Brefeld U, Scheffer T (2006) Semi-supervised learning for structured output variables, In: Proceedings of the 23rd international conference on Machine learning, ACM New York, NY, USA: Pittsburgh, Pennsylvania, pp 145-152.
18. Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: the ICML-2003 Workshop on The Continuum from Labeled to Unlabeled Data, pp 912-919.
19. Altun Y, McAllester D, Belkin M (2006) Maximum margin semi-supervised learning for structured variables. Adv Neural Inf Process Syst 18: 33-40.
20. F. Jiao, Wang S, Lee CH, Greiner R, Schuurmans D (2006) Semi-supervised conditional random fields for improved sequence segmentation and labeling, the 21st International Conference on Computational Linguistics, pp 209-216.
21. Small K, Roth D (2010) Margin-based active learning for structured predictions. Int J Mach Learn Cybern 1(1-4): 3-25.
22. McCallum A, Mann G, Druck G (2007) Generalized expectation criteria. Computer science technical note. University of Massachusetts, Amherst.
23. Mann GS, McCallum A (2007) Simple, robust, scalable semi-supervised learning via expectation regularization, In: Proceedings of the 24th international conference on Machine learning, ACM, pp 593-600.
24. Mann G, McCallum A (2010) Generalized expectation criteria for semi-supervised learning with weakly labeled data. J Mach Learn Res 11: 955-984.
25. Druck G, Mann G, McCallum A (2007) Reducing annotation effort using generalized expectation criteria (Technical Report 2007-62), University of Massachusetts, Amherst.
26. Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3: 993-1022.
27. Rabiner L (1989) A tutorial on Hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2): 257-286.
28. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, 282-289.
29. Wallach H (2004) Conditional random fields: an introduction. Technical Report MS-CIS-04-21, Department of Computer and Information Science, University of Pennsylvania, p 50.
30. Mann, G, McCallum A (2008) Generalized expectation criteria for semi-supervised learning of conditional random fields. In: Proceeding of Association of Computational Linguistics, pp 870-878.
31. Raghavan H, Madani O, Jones R (2006) Active learning with feedback on features and instances. J Mach Learn Res 7: 1655-1686.
32. Sun C et al (2007) Rich features based conditional random fields for biological named entities recognition. Comput Biol Med 37(9): 1327-1333.
33. Tsai T et al (2006) Integrating linguistic knowledge into a conditional random field framework to identify biomedical named entities. Expert Syst Appl 30(1): 117-128.
34. Settles B (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In: International Conference on Computational Linguistics. Geneva, Switzerland, pp 104-107.
35. Tsai T, Wu C, Hsu W (2005) Using maximum entropy to extract biomedical named entities without dictionaries. In: Proceedings of IJCNLP2005, pp 270-275.
36. Deerwester S et al (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6): 391-407.
37. Wenbo L, Le S, Dakun Z (2008) Text classification based on labeled-LDA model. Chinese J Comput 31(4): 620-627.
38. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1): 177-196.
39. Landauer TK, Foltz PPW, Laham D (1998) An introduction to latent semantic analysis. Discourse Process 25(2): 259-284.
40. Hofmann T (1999) Probabilistic latent semantic analysis. In: Proceeding of Uncertainty in Artificial Intelligence, Citeseer, pp 21-26.
41. Boyd-Graber J, Blei D, Zhu X (2007) A topic model for word sense disambiguation. In empirical methods in natural language processing, pp 1024-1033.
42. Toutanova K, Johnson M (2007) A Bayesian LDA-based model for semi-supervised part-of-speech tagging. Adv Neural Inf Process Syst 1521-1528.
43. Georgescul M, Clark A, Armstrong S (2008) A comparative study of mixture models for automatic topic segmentation of multiparty dialogues. In: Proceedings of the Third International Joint Conference on Natural Language Processing, pp 925-930.
44. Arora R, Ravindran B (2008) Latent dirichlet allocation based multi-document summarization. In: Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data. ACM, pp 91-97.
45. McCallum AK (2002) MALLET: a Machine Learning for Language Toolkit. http://mallet. cs. umass. edu.