Buchbeitrag
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.
- Language
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Englisch
- Subject
-
Autorschaft
Computerlinguistik
Sprache
- Event
-
Geistige Schöpfung
- (who)
-
Hitschler, Julian
van den Berg, Esther
Rehbein, Ines
- Event
-
Veröffentlichung
- (who)
-
Stroudsburg PA, USA : The Association for Computational Linguistics
- (when)
-
2018-10-02
- URN
-
urn:nbn:de:bsz:mh39-80252
- Last update
-
06.03.2025, 9:00 AM CET
Data provider
This object is provided by:
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Buchbeitrag
Associated
- Hitschler, Julian
- van den Berg, Esther
- Rehbein, Ines
- Stroudsburg PA, USA : The Association for Computational Linguistics
Time of origin
- 2018-10-02
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