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A systematic review of named entity recognition in biomedical texts

Journal of the Brazilian Computer Society volume 17, pages 103–116 (2011)Cite this article

Abstract

Biomedical Named Entities (NEs) are phrases or combinations of phrases that denote specific objects or groups of objects in the biomedical literature. Research on Named Entity Recognition (NER) is one of the most disseminated activities in the automatic processing of biomedical scientific articles. We analyzed articles relevant to NER in biomedical texts, in the period from 2007 to 2009, through a systematic review. The results identify the main methods in the recognition of Biomedical NEs, features and methodologies for a NER system implementation. Aside from the tendencies identified, some gaps are detected that may constitute opportunities for new studies in the area.

References

  1. Ananiadou S, McNaught J (2005) Text mining for biology and biomedicine. Artech House, Norwood

    Google Scholar 

  2. Chen L, Liu H, Friedman C (2005) Gene name ambiguity of eukaryotic nomenclatures. Bioinformatics 21(2):248–256

    Article  Google Scholar 

  3. Kozareva Z, Ferrandez O, Montoyo A, Munoz R, Suarez A (2007) Combining data-driven systems for improving named entity recognition. Data Knowl Eng 61(3):449–466

    Article  Google Scholar 

  4. Biolchini J, Mian PG, Natali ACC, Travassos GH (2005) Systematic review in software engineering. System Engineering and Computer Science Department COPPE/UFRJ, Technical report ES, 679(05)

  5. Kitchenham B (2004) Procedures for performing systematic reviews. Technical report, Keele University and NICTA

  6. Kim J-D, Ohta T, Tsuruoka Y, Tateisi Y, Collier N (2004) Introduction to the bio-entity recognition task at JNLPBA. In: JNLPBA’04: Proceedings of the international joint workshop on natural language processing in biomedicine and its applications, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 70–75

    Chapter  Google Scholar 

  7. Tsai RT-H, Wu S-H, Chou W-C, Lin Y-C, He D, Hsiang J, Sungand T-Y, Hsu W-L (2006) Various criteria in the evaluation of biomedical named entity recognition. BMC Bioinform 7:92

    Article  Google Scholar 

  8. Sasaki Y, Tsuruoka Y, McNaught J, Ananiadou S (2008) How to make the most of NE dictionaries in statistical NER. BMC Bioinform 9(Suppl 11):S5

    Article  Google Scholar 

  9. Sun B, Mitra P, Giles CL (2008) Mining, indexing, and searching for textual chemical molecule information on the web. In: WWW ’08: Proceedings of the 17th international conference on World Wide Web. ACM, New York, pp 735–744

    Chapter  Google Scholar 

  10. Hakenberg J, Plake C, Leaman R, Schroeder M, Gonzalez G (2008) Inter-species normalization of gene mentions with GNAT. Bioinformatics 24(16):126–132

    Article  Google Scholar 

  11. Tan H, Lambrix P (2009) Selecting an ontology for biomedical text mining. In: BioNLP ’09: Proceedings of the workshop on BioNLP, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 55–62

    Chapter  Google Scholar 

  12. Vlachos A (2007) Evaluating and combining biomedical named entity recognition systems. In: BioNLP ’07: Proceedings of the workshop on BioNLP 2007, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 199–206

    Chapter  Google Scholar 

  13. Jijkoun V, Khalid MA, Marx M, de Rijke M (2008) Named entity normalization in user generated content. In: AND ’08: proceedings of the second workshop on analytics for noisy unstructured text data. ACM, New York, pp 23–30

    Chapter  Google Scholar 

  14. Sarafraz F, Eales J, Mohammadi R, Dickerson J, Robertson D, Nenadic G (2009) Biomedical event detection using rules, conditional random fields and parse tree distances. In: BioNLP ’09: proceedings of the workshop on BioNLP, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 115–118

    Chapter  Google Scholar 

  15. Shi Z, Sarkar A, Popowich F (2007) Simultaneous identification of biomedical named-entity and functional relations using statistical parsing techniques. In: NAACL ’07: human language technologies 2007: the conference of the North American; Companion volume, Short papers on XX, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 161–164

    Google Scholar 

  16. Liu H, Blouin C, Keselj V (2009) Identifying interaction sentences from biological literature using automatically extracted patterns. In: BioNLP ’09: proceedings of the workshop on BioNLP, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 133–141

    Chapter  Google Scholar 

  17. Cohen KB, Verspoor K, Johnson HL, Roeder C, Ogren PV, Baumgartner WA Jr, White E, Tipney H, Hunter L (2009) High-precision biological event extraction with a concept recognizer. In: BioNLP ’09: proceedings of the workshop on BioNLP, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 50–58

    Chapter  Google Scholar 

  18. Aleman-Meza B, Nagarajan M, Ding L, Sheth A, Arpinar IB, Joshi A, Finin T (2008) Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection. ACM Trans Web 2(1):1–29

    Article  Google Scholar 

  19. Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge

    Google Scholar 

  20. Jurafsky D, Martin JH (2009) Speech and language processing, 2nd edn. Prentice-Hall, New York

    Google Scholar 

  21. Joachims T (1999) Advances in kernel methods: support vector learning. In: Making large-scale support vector machine learning practical. MIT Press, Cambridge, pp 169–184

    Google Scholar 

  22. Vlachos A (2007) Evaluating and combining biomedical named entity recognition systems. In: BioNLP ’07: proceedings of the workshop on BioNLP 2007, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 199–206

    Chapter  Google Scholar 

  23. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, pp 282–289

    Google Scholar 

  24. Groves RM, Fowler FJ Jr, Couper MP, Lepkowski JM, Singer E, Tourangeau R (2009) Survey methodology, 2nd edn. Wiley-Blackwell, New York

    Google Scholar 

  25. Overview articles. http://www.signalprocessingsociety.org/publications/overview-articles/

  26. Chan S-K, Lam W, Yu X (2007) A cascaded approach to biomedical named entity recognition using a unified model. In: Data mining. ICDM 2007. Seventh IEEE international conference on, Oct 2007, pp 93–102

    Google Scholar 

  27. Wang H, Zhao T, Liu J (2008) Multi-agent classifiers fusion strategy for biomedical named entity recognition. In: BioMedical engineering and informatics. BMEI 2008. International conference on, May 2008, vol 1, pp 311–315

    Chapter  Google Scholar 

  28. Kim J-D, Ohta T, Teteisi Y, Tsujii J (2003) GENIA corpus—a semantically annotated corpus for bio-textmining. Bioinformatics 19(Suppl 1):180–182

    Article  Google Scholar 

  29. Corbett P, Batchelor C, Teufel S (2007) Annotation of chemical named entities BioNLP 2007: biological, translational, and clinical language processing, Prague, Czech Republic, pp 57–64

  30. Corbett P, Copestake A (2008) Cascaded classifiers for confidence-based chemical named entity recognition. BMC Bioinformatics 9(Suppl 11):S4

    Article  Google Scholar 

  31. Sasaki Y, Tsuruoka Y, McNaught J, Ananiadou S (2008) How to make the most of ne dictionaries in statistical ner. In: BioNLP ’08: proceedings of the workshop on current trends in biomedical natural language processing, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 63–70

    Chapter  Google Scholar 

  32. Neves ML, Carazo JM, Pascual-Montano A (2009) Extraction of biomedical events using case-based reasoning. In: BioNLP ’09: proceedings of the workshop on BioNLP, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 68–76

    Chapter  Google Scholar 

  33. Li Y, Lin H, Yang Z (2007) Two approaches for biomedical text classification. In: Bioinformatics and biomedical engineering. ICBBE 2007. The 1st international conference on, July 2007, pp 310–313

    Chapter  Google Scholar 

  34. Viola P, Jones M (2001) Fast multi-view face detection. In: Proc of CVPR

    Google Scholar 

  35. Yoshida K, Tsujii J (2007) Reranking for biomedical named-entity recognition. In: BioNLP ’07: proceedings of the workshop on BioNLP 2007, Morristown, NJ, USA. Association for Computational Linguistics, Menlo Park, pp 209–216

    Chapter  Google Scholar 

  36. Gu B, Dahl V, Popowich F (2007) Recognizing biomedical named entities in the absence of human annotated corpora. In: Natural language processing and knowledge engineering. NLP-KE 2007. International conference on, August 30 2007–Sept 1, pp 74–81

    Chapter  Google Scholar 

  37. Cohen KB, Fox L, Ogren PV, Hunter L (2005) Empirical data on corpus design and usage in biomedical natural language processing. In: AMIA symposium, pp 156–160

    Google Scholar 

  38. Jurafsky D, Martin JH (2000) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Prentice Hall PTR, New York

    Google Scholar 

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Authors and Affiliations

  1. Feevale University, Novo Hamburgo, Brazil

    Rodrigo Rafael Villarreal Goulart

  2. PUCRS, Porto Alegre, Brazil

    Vera Lúcia Strube de Lima & Clarissa Castellã Xavier

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  1. Rodrigo Rafael Villarreal Goulart
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  2. Vera Lúcia Strube de Lima
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  3. Clarissa Castellã Xavier
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Correspondence to Rodrigo Rafael Villarreal Goulart.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Goulart, R.R.V., Strube de Lima, V.L. & Xavier, C.C. A systematic review of named entity recognition in biomedical texts. J Braz Comput Soc 17, 103–116 (2011). https://doi.org/10.1007/s13173-011-0031-9

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  • DOI: https://doi.org/10.1007/s13173-011-0031-9

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