Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
|Title:||Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning||Authors:||Leavy, Susan
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 embeddings; Text classification; Visualization; Government inquiry reports||DOI:||10.1007/978-3-030-10997-4_52||Other versions:||http://www.ecmlpkdd2018.org/
|Language:||en||Status of Item:||Not peer reviewed||Is part of:||Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.).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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