article

ME-based biomedical named entity recognition using lexical knowledge

Authors:

Kyung-Mi Park,

Seon-Ho Kim,

Hae-Chang Rim,

Young-Sook HwangAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 5, Issue 1

Pages 4 - 21

https://doi.org/10.1145/1131348.1131350

Published: 01 March 2006 Publication History

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Abstract

In this paper, we present a two-phase biomedical NE-recognition method based on a ME model: we first recognize biomedical terms and then assign appropriate semantic classes to the recognized terms. In the two-phase NE-recognition method, the performance of the term-recognition phase is very important, because the semantic classification is performed on the region identified at the recognition phase. In this study, in order to improve the performance of term recognition, we try to incorporate lexical knowledge into pre- and postprocessing of the term-recognition phase. In the preprocessing step, we use domain-salient words as lexical knowledge obtained by corpus comparison. In the postprocessing step, we utilize χ²-based collocations gained from Medline corpus. In addition, we use morphological patterns extracted from the training data as features for learning the ME-based classifiers. Experimental results show that the performance of NE-recognition can be improved by utilizing such lexical knowledge.

References

[1]

Berger, A., Pietra, S., and Pietra, V. 1996. A maximum-entropy approach to natural language processing. Computational Linguistics 22, 1, 39--71.

Crossref

Google Scholar

[2]

Brants, T. 2000. TnT---a statistical part-of-speech tagger. In Proceedings of the 6th Applied Natural Language Processing.

Crossref

Google Scholar

[3]

Chen, S. and Rosenfeld, R. 1999. A Gaussian prior for smoothing maximum entropy models. Technical Report CMUCS-99-108, Carnegie Mellon University.

Google Scholar

[4]

Finkel, J., Dingare, S., Nguyen, H., Nissim, M., Sinclair, G., and Manning, C. 2004. Exploiting context for biomedical entity recognition: from syntax to the web. Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004). 88--91.

Crossref

Google Scholar

[5]

Kim, J. D. and Tsujii, J. 2004. Word folding: taking the snapshot of words instead of the whole. In Proceedings of the 1st International Joint Conference on Natural Language Processing (IJCNLP-2004).

Crossref

Google Scholar

[6]

Kim, J. D., Ohta, T., Tsuruoka, Y., Tateisi, Y., and Collier, N. 2004. Introduction to the bio-entity recognition task at JNLPBA. In Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004). 70--75.

Crossref

Google Scholar

[7]

Kudoh, T. and Matsumoto, Y. 2000. Use of support vector learning for chunk identification. In Proceedings of the Conference on Natural Language Learning (CoNLL) 2000 Shared Task.

Crossref

Google Scholar

[8]

Lee, K. J., Hwang, Y. S., and Rim, H. C. 2003. Two-phase biomedical NE-recognition based on SVMs. In Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine. 33--40.

Crossref

Google Scholar

[9]

Settles, B. 2004. Biomedical named entity recognition using conditional random fields and novel feature sets. Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004). 104--107.

Crossref

Google Scholar

[10]

Song, Y., Kim, E. J., Lee, G. B., and Yi, B. K. 2004. Biomedical named entity recognition using conditional random fields and novel feature sets. Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004). 104--107.

Google Scholar

[11]

Zhou, G. and Su, J. 2004. Exploring deep knowledge resources in biomedical name recognition. Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-2004). 96--99.

Crossref

Google Scholar

[12]

Zhou, G., Zhang, J., Su, J., Shen, D., and Tan, C. 2004. Recognizing names in biomedical texts: a machine learning approach. Bioinformatics. 20, 1178--1190.

Crossref

Google Scholar

Cited By

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Prasad ASharma N(2021)Rule-Based Recognition of Associated Entities in Hindi Text: A Domain Centric ApproachInformation and Communication Technology for Competitive Strategies (ICTCS 2020)10.1007/978-981-16-0739-4_36(373-383)Online publication date: 27-Jul-2021
https://doi.org/10.1007/978-981-16-0739-4_36
Yadav SEkbal ASaha S(2019)Information theoretic-PSO-based feature selection: an application in biomedical entity extractionKnowledge and Information Systems10.1007/s10115-018-1265-z60:3(1453-1478)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10115-018-1265-z
Saha SEkbal ASikdar U(2018)Named entity recognition and classification in biomedical text using classifier ensembleInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2015.06795411:4(365-391)Online publication date: 23-Dec-2018
https://dl.acm.org/doi/10.1504/IJDMB.2015.067954
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Index Terms

ME-based biomedical named entity recognition using lexical knowledge
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Information

Published In

ACM Transactions on Asian Language Information Processing Volume 5, Issue 1

March 2006

88 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/1131348

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2006

Published in TALIP Volume 5, Issue 1

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Prasad ASharma N(2021)Rule-Based Recognition of Associated Entities in Hindi Text: A Domain Centric ApproachInformation and Communication Technology for Competitive Strategies (ICTCS 2020)10.1007/978-981-16-0739-4_36(373-383)Online publication date: 27-Jul-2021
https://doi.org/10.1007/978-981-16-0739-4_36
Yadav SEkbal ASaha S(2019)Information theoretic-PSO-based feature selection: an application in biomedical entity extractionKnowledge and Information Systems10.1007/s10115-018-1265-z60:3(1453-1478)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10115-018-1265-z
Saha SEkbal ASikdar U(2018)Named entity recognition and classification in biomedical text using classifier ensembleInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2015.06795411:4(365-391)Online publication date: 23-Dec-2018
https://dl.acm.org/doi/10.1504/IJDMB.2015.067954
Park KRim H(2018)Semantic Classification of Bio-Entities Incorporating Predicate-Argument FeaturesIEICE - Transactions on Information and Systems10.1093/ietisy/e91-d.4.1211E91-D:4(1211-1214)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1093/ietisy/e91-d.4.1211
Sikdar UEkbal ASaha S(2018)MODESoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1565-519:12(3529-3549)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-014-1565-5
Sikdar UEkbal ASaha S(2014)Differential evolution based multiobjective optimization for biomedical entity extraction2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2014.6968390(1039-1044)Online publication date: Sep-2014
https://doi.org/10.1109/ICACCI.2014.6968390
Ekbal ASaha SSikdar U(2014)On active annotation for named entity recognitionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-014-0275-87:4(623-640)Online publication date: 27-Jun-2014
https://doi.org/10.1007/s13042-014-0275-8
Palshikar G(2013)Techniques for Named Entity RecognitionBioinformatics10.4018/978-1-4666-3604-0.ch022(400-426)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-3604-0.ch022
Ekbal ASaha SSikdar U(2013)Biomedical named entity extraction: some issues of corpus compatibilitiesSpringerPlus10.1186/2193-1801-2-6012:1Online publication date: 12-Nov-2013
https://doi.org/10.1186/2193-1801-2-601
Palshikar G(2012)Techniques for Named Entity RecognitionCollaboration and the Semantic Web10.4018/978-1-4666-0894-8.ch011(191-217)Online publication date: 2012
https://doi.org/10.4018/978-1-4666-0894-8.ch011

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