Gene Entity Recognition of Full Text Articles

Noll, Manuel; Lété, Jonathan; Meyer, Patrick

doi:10.1145/3121138.3121167

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Gene Entity Recognition of Full Text Articles

Noll, Manuel; Lété, Jonathan; Meyer, Patrick

2017 • In ICBBS '17 Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

Peer reviewed

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https://hdl.handle.net/2268/214701

DOI
10.1145/3121138.3121167

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Keywords :

bibliomics; NER; Machine Learning; Automated gene-name identification

Abstract :

[en] Biomedical scientific literature is an unexploited treasure. Due to the staggering number of publications it is literally intractable to gather manually all information. Automatized information extraction (IE) is therefore key. An important subtask is the recognition of names in the text as specific entities ( named entity recognition, NER). NER for genes in biomedical literature is a challenging task. This paper reports preliminary results for the identification of gene names in full text with the naive Bayes, support vector machine and the random forest algorithm, showing that there is no loss on performance compared to the gene NER restricted to abstracts.

Disciplines :

Life sciences: Multidisciplinary, general & others
Computer science

Author, co-author :

Noll, Manuel ; Université de Liège - ULiège > Département des sciences de la vie > Biologie des systèmes et bioinformatique

Lété, Jonathan ; Université de Liège - ULiège > Master bioch. & biol. mol. & cel., à fin.

Meyer, Patrick ; Université de Liège - ULiège > Département des sciences de la vie > Biologie des systèmes et bioinformatique

Language :

English

Title :

Gene Entity Recognition of Full Text Articles

Publication date :

June 2017

Event name :

ICBBS '17: 6th International Conference on Bioinformatics and Biomedical Science

Event date :

June 22 - 24, 2017

Audience :

International

Main work title :

ICBBS '17 Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

Publisher :

ACM, New York, United States

ISBN/EAN :

978-1-4503-5222-2

Pages :

162-167

Peer reviewed :

Peer reviewed

Additional URL :

https://dl.acm.org/citation.cfm?id=3121138

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]

Available on ORBi :

since 02 October 2017

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