Unsupervised Information Extraction from Behaviour Change Literature
Debasis Ganguly, Deleris Léa A., Mac Aonghusa Pol, Alison J. Wright, Ailbhe N. Finnerty, Emma Norris, Marta Moreira Marques, Michie Susan · Studies in health technology and informatics · 2018
This paper describes our approach to construct a scalable system for unsupervised information extraction from the behaviour change intervention literature. Due to the many different types of attribute to be extracted, we adopt a passage retrieval based framework that provides the most likely value for an attribute. Our proposed method is capable of addressing variable length passage sizes and different validation criteria for the extracted values corresponding to each attribute to be found. We evaluate our approach by constructing a manually annotated ground-truth from a set of 50 research papers with reported studies on smoking cessation.