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Authorship attribution with convolutional neural networks and POS-eliding

  • We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.

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Metadaten
Author:Julian Hitschler, Esther van den Berg, Ines Rehbein
URN:urn:nbn:de:bsz:mh39-80252
URL:http://aclweb.org/anthology/W17-4907
DOI:https://doi.org/10.18653/v1/W17-4907
ISBN:978-1-945626-99-9
Parent Title (English):Proceedings of the Workshop on Stylistic Variation (EMNLP 2017). September 8, 2017 Copenhagen, Denmark
Publisher:The Association for Computational Linguistics
Place of publication:Stroudsburg PA, USA
Document Type:Part of a Book
Language:English
Year of first Publication:2017
Date of Publication (online):2018/10/02
Publicationstate:Veröffentlichungsversion
Reviewstate:Peer-Review
Tag:Part-of-Speech-Tagging
GND Keyword:Autorschaft; Computerlinguistik
First Page:53
Last Page:28
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Leibniz-Classification:Sprache, Linguistik
Open Access?:Ja
Licence (English):License LogoCreative Commons - Attribution 4.0 International