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A comparative study for biomedical named entity recognition

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Abstract

With high-throughput technologies applied in biomedical research, the quantity of biomedical literatures grows exponentially. It becomes more and more important to quickly as well as accurately extract knowledge from manuscripts, especially in the era of big data. Named entity recognition (NER), aiming at identifying chunks of text that refers to specific entities, is essentially the initial step for information extraction. In this paper, we will review the three models of biomedical NER and two famous machine learning methods, Hidden Markov Model and Conditional Random Fields, which have been widely applied in bioinformatics. Based on these two methods, six excellent biomedical NER tools are compared in terms of programming language, feature sets, underlying mathematical methods, post-processing techniques and flowcharts. Experimental results of these tools against two widely used corpora, GENETAG and JNLPBA, are conducted. The comparison varies from different entity types to the overall performance. Furthermore, we put forward suggestions about the selection of Bio-NER tools for different applications.

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References

  1. Rosario B, Hearst MA (2004) Classifying semantic relations in bioscience texts. In: Proceedings 42nd annual meeting association computional linguistics. doi:10.3115/1218955.1219010

  2. Chiang J-H, Yu H-C (2003) MeKE: discovering the functions of gene products from biomedical literature via sentence alignment. Bioinformatics 19:1417–1422. doi:10.1093/bioinformatics/btg160

    Article  Google Scholar 

  3. Ciaramita M, Gangemi A, Ratsch E et al (2005) Unsupervised learning of semantic relations between concepts of a molecular biology ontology. In: IJCAI. pp 659–664

  4. Zhou G, Su J (2002) Named entity recognition using an hmm-based chunk tagger. In: Proceedings 40th annual meeting association computational linguistics. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 473–480

  5. Collier N, Nobata C, Tsujii J (2000) Extracting the names of genes and gene products with a hidden markov model. In: Proceedings 18th conference computional linguistics, vol 1. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 201–207

  6. Gaizauskas R, Demetriou G, Humphreys K (2000) Term recognition and classification in biological science journal articles. In: Proceedings computional terminology for medical and biological applications workshop 2nd international conference NLP. pp 37–44

  7. Kazama J, Makino T, Ohta Y, Tsujii J (2002) Tuning support vector machines for biomedical named entity recognition. In: Proceedings ACL-02 workshop natural language processing in the biomedicine domain, vol 3. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1–8

  8. Takeuchi K, Collier N (2002) Use of support vector machines in extended named entity recognition. In: Proceedings 6th Confernce Natural Language Learn, vol 20. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1–7

  9. Zhou G, Zhang J, Su J et al (2004) Recognizing names in biomedical texts: a machine learning approach. Bioinformatics 20:1178–1190. doi:10.1093/bioinformatics/bth060

    Article  Google Scholar 

  10. Fukuda K, Tamura A, Tsunoda T, Takagi T (1998) Toward information extraction: identifying protein names from biological papers. Pacific Symposium Biocomputing Pacific Symposium Biocomputional. pp 707–718

  11. Nobata C, Collier N, Tsujii J (1999) Automatic term identification and classification in biology texts. In: Proceedings 5th NLPRS. pp 369–374

  12. Chang JT, Schütze H, Altman RB (2002) Creating an online dictionary of abbreviations from MEDLINE. J Am Med Inform Assoc JAMIA 9:612–620

    Article  Google Scholar 

  13. Liu H, Aronson AR, Friedman C (2002) A study of abbreviations in MEDLINE abstracts. In: Proceedings AMIA annual symposium AMIA symposium. pp 464–468

  14. Sondhi P A survey on named entity extraction in the biomedical domain. Available online at http://sifaka.cs.uiuc.edu/~sondhi1/survey1.pdf

  15. Tsuruoka Y, Tsujii J (2003) Boosting precision and recall of dictionary-based protein name recognition. In: Proceedings ACL 2003 workshop natural language processing biomedicine, vol 13. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 41–48

  16. Yang Z, Lin H, Li Y (2008) Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature. Comput Biol Chem 32:287–291 (2008.03.008)

    Article  MATH  Google Scholar 

  17. Proux D, Rechenmann F, Julliard V et al (1998) Detecting gene symbols and names in biological texts: a first step toward pertinent information extraction. Genome Inform Workshop Genome Inform 9:72–80

    Google Scholar 

  18. Tsai RT, Sung C-L, Dai H-J et al (2006) NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition. BMC Bioinform 7:S11. doi:10.1186/1471-2105-7-S5-S11

    Article  Google Scholar 

  19. He X, Zemel RS, Carreira-Perpindn MA (2004) Multiscale conditional random fields for image labeling. In: Proceedings 2004 IEEE computional society conference computional vis. pattern recognition 2004 CVPR 2004, vol 2. pp II–695–II–702

  20. Sha F, Pereira F (2003) Shallow parsing with conditional random fields. In: Proceedings 2003 conference North America chapter association computional linguistics human language technology, vol 1. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 134–141

  21. Settles B (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings international joint workshop natural language processing biomedicine its application. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 104–107

  22. Settles B (2005) ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics 21:3191–3192. doi:10.1093/bioinformatics/bti475

    Article  Google Scholar 

  23. Baldwin B, Carpenter B (2003) LingPipe. World Wide Web Httpalias-Comlingpipe

  24. Leaman R, Gonzalez G, others (2008) BANNER: an executable survey of advances in biomedical named entity recognition. In: Pacific Symposium Biocomputing. pp 652–663

  25. Cho HC (2010) NERsuite: a named entity recognition toolkit. Tsujii Laboratory, Department of Information Science, University of Tokyo, Tokyo, Japan. http://nersuite.nlplab.org. http://nersuite.nlplab.org/. Accessed 14 Nov 2014

  26. Campos D, Matos S, Oliveira JL (2013) Gimli: open source and high-performance biomedical name recognition. BMC Bioinform 14:54. doi:10.1186/1471-2105-14-54

    Article  Google Scholar 

  27. Tsuruoka Y (2006) GENIA tagger: Part-of-speech tagging, shallow parsing, and named entity recognition for biomedical text

  28. Tsuruoka Y, Tsujii J (2005) Bidirectional inference with the easiest-first strategy for tagging sequence data. In: Proceedings conference human language technology empirical methods natural language processing. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 467–474

  29. Tanabe L et al (2005) GENETAG: a tagged corpus for gene/protein named entity recognition. BMC bioinform 6(Suppl 1):S3

    Article  Google Scholar 

  30. Zhou X, Zhang X, Hu X (2007) Dragon toolkit: incorporating auto-learned semantic knowledge into large-scale text retrieval and mining. In: Tools artificial intelligence 2007 ICTAI 2007 19th IEEE international Conference on IEEE. pp 197–201

  31. McCallum AK (2002) Mallet: a machine learning for language toolkit. Available online at https://people.cs.umass.edu/~mccallum/mallet/

  32. Sagae K, Tsujii J (2007) Dependency parsing and domain adaptation with LR models and parser ensembles. In: EMNLP-CoNLL. pp 1044–1050

  33. Liu H, Hu Z-Z, Zhang J, Wu C (2006) BioThesaurus: a web-based thesaurus of protein and gene names. Bioinformatics 22:103–105. doi:10.1093/bioinformatics/bti749

    Article  Google Scholar 

  34. Kim J-D, Ohta T, Tsuruoka Y et al (2004) Introduction to the bio-entity recognition task at JNLPBA. In: Proceeding international joint workshop natural language processing biomedicine its applications. Association for Computational Linguistics, pp 70–75

  35. Smith L, Tanabe LK, Ando RJ et al (2008) Overview of BioCreative II gene mention recognition. Genome Biol 9:S2

    Article  Google Scholar 

  36. Dingare S, Nissim M, Finkel J et al (2005) A system for identifying named entities in biomedical text: how results from two evaluations reflect on both the system and the evaluations. Comp Funct Genom 6:77–85. doi:10.1002/cfg.457

    Article  Google Scholar 

  37. Zhang S, Elhadad N (2013) Unsupervised biomedical named entity recognition: experiments with clinical and biological texts. J Biomed Inform. doi:10.1016/j.jbi.2013.08.004

    Google Scholar 

  38. Tang Z, Jiang L, Yang L et al (2015) CRFs based parallel biomedical named entity recognition algorithm employing MapReduce framework. Clust Comput 18:493–505. doi:10.1007/s10586-015-0426-z

    Article  Google Scholar 

  39. Li K, Ai W, Tang Z et al (2015) Hadoop recognition of biomedical named entity using conditional random fields. In: IEEE transaction parallel distribution system. pp 1–1. doi:10.1109/TPDS.2014.2368568

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Acknowledgments

This paper is supported by the National Key Basic Research Program of China (No. 2015CB453000), National Natural Science Foundation of China (Nos. 61572228, 41101376, 61272207 and 61300147), and the Science Technology Development Project of Jilin Province of China (20130101070JC, 20130522106JH and 20140520070JH).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, People’s Republic of China

    Xu Wang & Renchu Guan

  2. College of Earth Sciences, Jilin University, 2699 Qianjin Street, Changchun, 130012, People’s Republic of China

    Chen Yang

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  1. Xu Wang
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  2. Chen Yang
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  3. Renchu Guan
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Correspondence to Renchu Guan.

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Wang, X., Yang, C. & Guan, R. A comparative study for biomedical named entity recognition. Int. J. Mach. Learn. & Cyber. 9, 373–382 (2018). https://doi.org/10.1007/s13042-015-0426-6

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