Article

Boosting performance of bio-entity recognition by combining results from multiple systems

Authors:

Xiangji HuangAuthors Info & Claims

BIOKDD '05: Proceedings of the 5th international workshop on Bioinformatics

Pages 76 - 83

https://doi.org/10.1145/1134030.1134044

Published: 21 August 2005 Publication History

Abstract

The task of biomedical named-entity recognition is to identify technical terms in the domain of biology that are of special interest to domain experts. While numerous algorithms have been proposed for this task, biomedical named-entity recognition remains a challenging task and an active area of research, as there is still a large accuracy gap between the best algorithms for biomedical named-entity recognition and those for general newswire named-entity recognition. The reason for such discrepancy in accuracy results is generally attributed to inadequate feature representations of individual entity recognition systems and external domain knowledge.In order to take advantage of the rich feature representations and external domain knowledge used by different systems, we propose several Meta biomedical named-entity recognition algorithms that combine recognition results of various recognition systems. The proposed algorithms -- majority vote, unstructured exponential model and conditional random field -- were tested on the GENIA biomedical corpus. Empirical results show that the F score can be improved from 0.72, which is attained by the best individual system, to 0.96 by our Meta entity recognition approach.

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Cited By

Bornet CKaplan F(2017)A Simple Set of Rules for Characters and Place Recognition in French NovelsFrontiers in Digital Humanities10.3389/fdigh.2017.000064Online publication date: 31-Mar-2017
https://doi.org/10.3389/fdigh.2017.00006
Korkontzelos IPiliouras DDowsey AAnaniadou S(2015)Boosting drug named entity recognition using an aggregate classifierArtificial Intelligence in Medicine10.1016/j.artmed.2015.05.00765:2(145-153)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.artmed.2015.05.007
Lund WRingger EWalker D(2014)How well does multiple OCR error correction generalize?Document Recognition and Retrieval XXI10.1117/12.2042502(90210A)Online publication date: 3-Feb-2014
https://doi.org/10.1117/12.2042502
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Index Terms

Boosting performance of bio-entity recognition by combining results from multiple systems
1. Information systems
  1. Information retrieval

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cover image ACM Other conferences

BIOKDD '05: Proceedings of the 5th international workshop on Bioinformatics

August 2005

79 pages

ISBN:1595932135

DOI:10.1145/1134030

General Chair:
Mohammed Zaki
Rensselaer Polytechnic Institute
,
Program Chairs:
Srinivasan Parthasarathy
Ohio State University
,
Wei Wang
University of North Carolina

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 21 August 2005

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KDD05

KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21, 2005

Illinois, Chicago

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Overall Acceptance Rate 7 of 16 submissions, 44%

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Cited By

Bornet CKaplan F(2017)A Simple Set of Rules for Characters and Place Recognition in French NovelsFrontiers in Digital Humanities10.3389/fdigh.2017.000064Online publication date: 31-Mar-2017
https://doi.org/10.3389/fdigh.2017.00006
Korkontzelos IPiliouras DDowsey AAnaniadou S(2015)Boosting drug named entity recognition using an aggregate classifierArtificial Intelligence in Medicine10.1016/j.artmed.2015.05.00765:2(145-153)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.artmed.2015.05.007
Lund WRingger EWalker D(2014)How well does multiple OCR error correction generalize?Document Recognition and Retrieval XXI10.1117/12.2042502(90210A)Online publication date: 3-Feb-2014
https://doi.org/10.1117/12.2042502
Matei SBertino EZhu MLiu CSi LBritt B(2014)A Research Agenda for the Study of Entropic Social Structural Evolution, Functional Roles, Adhocratic Leadership Styles, and Credibility in Online Organizations and Knowledge MarketsRoles, Trust, and Reputation in Social Media Knowledge Markets10.1007/978-3-319-05467-4_1(3-33)Online publication date: 3-Sep-2014
https://doi.org/10.1007/978-3-319-05467-4_1
Chen LOrtona SOrsi GBenedikt M(2013)Aggregating semantic annotatorsProceedings of the VLDB Endowment10.14778/2536258.25362616:13(1486-1497)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.14778/2536258.2536261
Pfeifer KMeinecke JWeippl EIndrawan-Santiago MSteinbauer MKotsis GKhalil I(2013)Identifying the TruthProceedings of International Conference on Information Integration and Web-based Applications & Services10.1145/2539150.2539160(565-574)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1145/2539150.2539160
Piliouras DKorkontzelos IDowsey AAnaniadou S(2013)Dealing with Data Sparsity in Drug Named Entity RecognitionProceedings of the 2013 IEEE International Conference on Healthcare Informatics10.1109/ICHI.2013.9(14-21)Online publication date: 9-Sep-2013
https://dl.acm.org/doi/10.1109/ICHI.2013.9
Yao LSun CWu YWang XWang X(2011)Biomedical named entity recognition using generalized expectation criteriaInternational Journal of Machine Learning and Cybernetics10.1007/s13042-011-0022-32:4(235-243)Online publication date: 25-Aug-2011
https://doi.org/10.1007/s13042-011-0022-3
Zhang XZou JLe DThoma GDoermann DGovindaraju VLopresti DNatarajan P(2010)Investigator name recognition from medical journal articlesProceedings of the 9th IAPR International Workshop on Document Analysis Systems10.1145/1815330.1815346(121-128)Online publication date: 9-Jun-2010
https://dl.acm.org/doi/10.1145/1815330.1815346
Dai HChang YTzong-Han Tsai RHsu W(2010)New Challenges for Biological Text-Mining in the Next DecadeJournal of Computer Science and Technology10.1007/s11390-010-9313-525:1(169-179)Online publication date: 20-Jan-2010
https://doi.org/10.1007/s11390-010-9313-5
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