Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning

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Title: Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
Authors: Leavy, Susan
Pine, Emilie
Keane, Mark T.
Permanent link: http://hdl.handle.net/10197/10321
Date: 18-Jan-2019
Online since: 2019-05-07T13:36:07Z
Abstract: We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries. Despite their historical significance and potential societal impact, key findings of inquiries are often hidden within lengthy documents and remain inaccessible to the general public. We transform the findings of the Irish government’s inquiry into industrial schools and through the use of word embedding, text classification and visualization, present an interactive web-based platform that enables the exploration of the text in new ways to uncover new historical insights.
Type of material: Conference Publication
Publisher: ECML-PKDD
Series/Report no.: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III
Copyright (published version): 2019 Springer Nature Switzerland AG
Keywords: Word embeddingsText classificationVisualizationGovernment inquiry reports
DOI: 10.1007/978-3-030-10997-4_52
Other versions: http://www.ecmlpkdd2018.org/
https://link.springer.com/chapter/10.1007/978-3-030-10997-4_52
Language: en
Status of Item: Not peer reviewed
Is part of: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.). Machine Learning and Knowledge Discovery in Databases
Conference Details: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018 (ECML-PKDD 2018), Dublin, Ireland, 10-14 September 2018
ISBN: 978-3-030-10996-7
Appears in Collections:Computer Science Research Collection

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