Using word embeddings to explore the Aboriginality discourse in a corpus of Australian Aboriginal autobiographies

Reference : Using word embeddings to explore the Aboriginality discourse in a corpus of Australia...


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Arts & humanities : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/45636

Title :	Using word embeddings to explore the Aboriginality discourse in a corpus of Australian Aboriginal autobiographies
Language :	English
Author, co-author :	Kamlovskaya, Ekaterina [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)]
	Schommer, Christoph [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	29-Sep-2020
Peer reviewed :	No
Audience :	International
Event name :	Synergies: Bridging the Gap Between Traditional and Digital Literary Studies
Event date :	from 27-09-2020 to 29-09-2020
Event organizer :	Network for Digital Literary Studies, University of Southern Denmark
Event country :	Denmark
Keywords :	[en] digital humanities ; text mining ; Indigenous studies ; digital history ; word embeddings
Abstract :	[en] This submission presents intermediate results of the PhD project analysing discourses in a corpus of Australian Aboriginal autobiographies with word embedding modelling.
Research centres :	Luxembourg Centre for Contemporary and Digital History (C2DH) > Doctoral Training Unit (DTU)
Funders :	Fonds National de la Recherche - FnR
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/45636
FnR project :	FnR ; FNR10929115 > Andreas Fickers > DHH > Digital History and Hermeneutics > 01/03/2017 > 31/08/2023 > 2016

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	File	Commentary	Version	Size	Access
Open access	Synergies_2020_Kamlovskaya_Schommer.pdf		Author preprint	33.78 kB	View/Open

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