| Literature DB >> 31258970 |
Debasis Ganguly1, Yufang Hou1, Le A A Deleris1, Francesca Bonin1.
Abstract
We describe an information extraction (IE) approach for knowledge base population of behavior change scientific intervention findings. In this paper, we focus on building a system able to characterize the specific intervention techniques that are undertaken within behavior change intervention studies. We have investigated three different configurations of a general information retrieval based framework for information extraction: a) an unsupervised approach that hinges on specification of a query for each attribute to be extracted and a few parameters for rule-based post-processing; b) a semi-supervised approach, which uses a part of the ground-truth annotations as a training set to automatically learn optimal representation of the queries; and c) a supervised approach that replaces the rule-based post processing by a text classifier. To train and evaluate our system, we make use of a ground-truth data set annotated by behavior science experts. This dataset consists of a total of 226 research papers on smoking cessation.Year: 2019 PMID: 31258970 PMCID: PMC6568066
Source DB: PubMed Journal: AMIA Jt Summits Transl Sci Proc