Deep Learning Powered Question-Answering Framework for Organizations Digital Transformation
Carvalho, Nuno and Soares Barbosa, Luís, "Deep Learning Powered Question-Answering Framework for Organizations Digital Transformation" 12th International Conference on Theory and Practice of Electronic Governance (ICEGOV2019), Melbourne, 2019/04/03-05.
Document type:
Conference Publication
Collection:
-
Attached Files (Some files may be inaccessible until you login with your UNU Collections credentials) Name Description MIMEType Size Downloads p076-Ramos-Carvalho.pdf Full paper (open access) application/pdf 669.29KB -
Sub-type Conference paper Author Carvalho, Nuno
Soares Barbosa, LuísTitle Deep Learning Powered Question-Answering Framework for Organizations Digital Transformation Event Series International Conference on Theory and Practice of Electronic Governance (ICEGOV) Publication Date 2019-05 Place of Publication New York Publisher ACM Press Pages 76-79 Title of Event 12th International Conference on Theory and Practice of Electronic Governance (ICEGOV2019) Date of Event 2019/04/03-05 Place of Event Melbourne Language eng Abstract In the context of digital transformation by governments, the public sector and other organizations, many information is moving to digital platforms. Chatbots and similar question-answering systems are becoming popular to answer information queries, opposed to browsing online repositories or webpages. State-of-the-art approaches for these systems may be laborious to implement, hard to train and maintain, and also require a high level of expertise. This work explores the definition of a generic framework to systematically build question-answering systems. A sandbox implementation of this framework enables the deployment of turnkey systems, directly from already existing collections of documents. These systems can then be used to provide a question-answering system communication channel to enrich the organization digital presence. Keyword deep learning
question-answering system
digital transformationCopyright Holder ACM Press Copyright Year 2019 Copyright type All rights reserved ISBN 9781450366441 DOI 10.1145/3326365.3326375 -
Citation counts Search Google Scholar Access Statistics: 728 Abstract Views, 643 File Downloads - Detailed Statistics Created: Tue, 21 May 2019, 19:14:44 JST by Mario Peixoto on behalf of UNU EGOV