Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification

Muhammad Amith; Yaoyun Zhang; Hua Xu; Cui Tao

doi:10.1007/978-3-319-60045-1_40

Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification

Muhammad Amith, Yaoyun Zhang, Hua Xu, Cui Tao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Statistical and machine learning approaches to named entity recognition have risen to prominence in the field of natural language processing. Certain named entities, specifically biomedical software, is a challenge to identify as a named entity. One direction is investigating the use of contextual semantic information to assist in this task as alluded to by previous researchers. We introduce an ontology-driven method that experiments with both information extraction and inherited features of ontologies (e.g., embedded semantic relationships and links to entities) to automatically identify familiar and unfamiliar software names. We evaluated this method with a set of biomedical research abstracts containing software entities. Our proposed approach could be used to further augment other named entity recognition methods.

Original language	English (US)
Title of host publication	Advances in Artificial Intelligence
Subtitle of host publication	From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings
Editors	Moonis Ali, Salem Benferhat, Karim Tabia
Publisher	Springer Verlag
Pages	386-395
Number of pages	10
ISBN (Print)	9783319600444
DOIs	https://doi.org/10.1007/978-3-319-60045-1_40
State	Published - 2017
Externally published	Yes
Event	30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017 - Arras, France Duration: Jun 27 2017 → Jun 30 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10351 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017
Country/Territory	France
City	Arras
Period	6/27/17 → 6/30/17

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-60045-1_40

Cite this

Amith, M., Zhang, Y., Xu, H., & Tao, C. (2017). Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification. In M. Ali, S. Benferhat, & K. Tabia (Eds.), Advances in Artificial Intelligence: From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings (pp. 386-395). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10351 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-60045-1_40

Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification. / Amith, Muhammad; Zhang, Yaoyun; Xu, Hua et al.
Advances in Artificial Intelligence: From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. ed. / Moonis Ali; Salem Benferhat; Karim Tabia. Springer Verlag, 2017. p. 386-395 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10351 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Amith, M, Zhang, Y, Xu, H & Tao, C 2017, Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification. in M Ali, S Benferhat & K Tabia (eds), Advances in Artificial Intelligence: From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10351 LNCS, Springer Verlag, pp. 386-395, 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, 6/27/17. https://doi.org/10.1007/978-3-319-60045-1_40

Amith M, Zhang Y, Xu H, Tao C. Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification. In Ali M, Benferhat S, Tabia K, editors, Advances in Artificial Intelligence: From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. Springer Verlag. 2017. p. 386-395. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-60045-1_40

Amith, Muhammad ; Zhang, Yaoyun ; Xu, Hua et al. / Knowledge-based approach for named entity recognition in biomedical literature : A use case in biomedical software identification. Advances in Artificial Intelligence: From Theory to Practice - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. editor / Moonis Ali ; Salem Benferhat ; Karim Tabia. Springer Verlag, 2017. pp. 386-395 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Statistical and machine learning approaches to named entity recognition have risen to prominence in the field of natural language processing. Certain named entities, specifically biomedical software, is a challenge to identify as a named entity. One direction is investigating the use of contextual semantic information to assist in this task as alluded to by previous researchers. We introduce an ontology-driven method that experiments with both information extraction and inherited features of ontologies (e.g., embedded semantic relationships and links to entities) to automatically identify familiar and unfamiliar software names. We evaluated this method with a set of biomedical research abstracts containing software entities. Our proposed approach could be used to further augment other named entity recognition methods.",

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note = "Funding Information: Research was partially supported by the National Library Of Medicine of the National Institutes of Health under Award Number R01LM011829 and R01AI130460, by the National Institutes of Health (NIH) through the NIH Big Data to Knowledge, Grant 1U24AI117966-01, and by the Cancer Prevention Research Institute of Texas (CPRIT) Training Grant #RP160015. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017 ; Conference date: 27-06-2017 Through 30-06-2017",

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Knowledge-based approach for named entity recognition in biomedical literature: A use case in biomedical software identification

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