A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment

BMC Bioinformatics

Research Article

Yufan Guo¹  Anna Korhonen¹  Maria Liakata²  Johan Hogberg³  Ilona Silins³  Ulla Stenius³ 

[1] Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, CB3 0FD, Cambridge, UK;Department of Computer Science, Aberystwyth University, SY23 3DB, UK;Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden;

关键词: Support Vector Machine;    User Test;    Conditional Random Field;    Cancer Risk Assessment;    Annotation Guideline;   

DOI  :  10.1186/1471-2105-12-69

 received in 2010-08-09, accepted in 2011-03-08,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundMany practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking.MethodsWe take three schemes of different type and granularity - those based on section names, Argumentative Zones (AZ) and Core Scientific Concepts (CoreSC) - and evaluate their usefulness for a real-life task which focuses on biomedical abstracts: Cancer Risk Assessment (CRA). We annotate a corpus of CRA abstracts according to each scheme, develop classifiers for automatic identification of the schemes in abstracts, and evaluate both the manual and automatic classifications directly as well as in the context of CRA.ResultsOur results show that for each scheme, the majority of categories appear in abstracts, although two of the schemes (AZ and CoreSC) were developed originally for full journal articles. All the schemes can be identified in abstracts relatively reliably using machine learning. Moreover, when cancer risk assessors are presented with scheme annotated abstracts, they find relevant information significantly faster than when presented with unannotated abstracts, even when the annotations are produced using an automatic classifier. Interestingly, in this user-based evaluation the coarse-grained scheme based on section names proved nearly as useful for CRA as the finest-grained CoreSC scheme.ConclusionsWe have shown that existing schemes aimed at capturing information structure of scientific documents can be applied to biomedical abstracts and can be identified in them automatically with an accuracy which is high enough to benefit a real-life task in biomedicine.

【 授权许可】

Unknown   
© Guo 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.