Detection of interaction articles and experimental methods in biomedical literature

BMC Bioinformatics

Research

Gerold Schneider¹  Fabio Rinaldi¹  Simon Clematide¹ 

[1] Institute of Computational Linguistics, University of Zurich, 8050, Zurich, Switzerland;

关键词: Matthews Correlation Coefficient;    Training Corpus;    Shared Task;    Natural Language Processing Tool;    Text Mining System;   

DOI  :  10.1186/1471-2105-12-S8-S13

来源: Springer

【 摘 要 】

BackgroundThis 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).ResultsTwo 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).ConclusionsThe 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.

【 授权许可】

Unknown   
© Schneider et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.