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.

• Publication year

  2017

• Venue

  Unknown venue

• Publication date

  Unknown publication date

• Fields of study

  Computer Science

• Identifiers
  DOI 10.18653/v1/W17-4907
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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