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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-52959

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

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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.


5 citations in Web of Science®
12 citations in Scopus®
Google Scholar™



101 downloads since deposited on 06 Jan 2012
15 downloads since 12 months

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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
Date:3 October 2011
Deposited On:06 Jan 2012 11:34
Last Modified:13 May 2016 07:08
Publisher:BioMed Central
Funders:Swiss National Science Foundation (grants 100014 - 118396/1 and 105315 - 130558/1), NITAS/TMS, Text Mining Services, Novartis Pharma AG, Basel
Publisher DOI:10.1186/1471-2105-12-S8-S13

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