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Detection of interaction articles and experimental methods in biomedical literature


Schneider, Gerold; Clematide, Simon; Rinaldi, Fabio (2011). Detection of interaction articles and experimental methods in biomedical literature. BMC Bioinformatics, 12(Suppl 8):S13.

Abstract

Background: This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein- protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT). Results: Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5). Conclusions: The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches.

Background: This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein- protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT). Results: Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5). Conclusions: The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches.

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5 citations in Web of Science®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > English Department
06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
820 English & Old English literatures
410 Linguistics
Uncontrolled Keywords:BioCreative III ; Text Mining ; Information Extraction ; Document classification ; Detection of experimental methods
Language:English
Date:3 October 2011
Deposited On:06 Jan 2012 11:34
Last Modified:24 Nov 2016 10:03
Publisher:BioMed Central
ISSN:1471-2105
Funders:Swiss National Science Foundation (grants 100014 - 118396/1 and 105315 - 130558/1), NITAS/TMS, Text Mining Services, Novartis Pharma AG, Basel
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/1471-2105-12-S8-S13
PubMed ID:22151872
Permanent URL: https://doi.org/10.5167/uzh-52959

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