Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-52959
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.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||06 Faculty of Arts > Institute of English Studies|
06 Faculty of Arts > Institute of Computational Linguistics
|DDC:||000 Computer science, knowledge & systems|
820 English & Old English literatures
|Uncontrolled Keywords:||BioCreative III ; Text Mining ; Information Extraction ; Document classification ; Detection of experimental methods|
|Date:||03 October 2011|
|Deposited On:||06 Jan 2012 12:34|
|Last Modified:||09 Dec 2013 03:46|
|Funders:||Swiss National Science Foundation (grants 100014 - 118396/1 and 105315 - 130558/1), NITAS/TMS, Text Mining Services, Novartis Pharma AG, Basel|
|Citations:||Web of Science®. Times Cited: 5|
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