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

Background: Many 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 ssigns 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 (BIOTM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking. Methods: We 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 dical abstracts: Cancer Risk Assessment (CRA). We annotate a corpus of CRA abstracts according to each d...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Yufan Guo, Anna Korhonen, Maria Liakata, Ilona Silins, Johan Hogberg, Ulla Stenius
• Keywords: Abstracts, Artificial Intelligence, BMCBI 2011, Information, Real-life Task