Information Extraction of Behavior Change Intervention Descriptions.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science(2019)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要