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Unsupervised Information Extraction from Behaviour Change Literature

Ganguly, D; Deleris, LA; Mac Aonghusa, P; Wright, AJ; Finnerty, AN; Norris, E; Marques, MM; (2018) Unsupervised Information Extraction from Behaviour Change Literature. Studies in Health Technology and Informatics , 247 pp. 680-684. 10.3233/978-1-61499-852-5-680. Green open access

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Abstract

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.

Type: Article
Title: Unsupervised Information Extraction from Behaviour Change Literature
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/978-1-61499-852-5-680
Publisher version: http://dx.doi.org/10.3233/978-1-61499-852-5-680
Language: English
Additional information: © 2018 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Keywords: Behavior Change, Information Extraction, Smoking Cessation
URI: http://discovery.ucl.ac.uk/id/eprint/10048438
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