Leveraging Discourse Information Effectively for Authorship Attribution

Elisa Ferracane, Su Wang, Raymond Mooney


Abstract
We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution. We present a novel method to embed discourse features in a Convolutional Neural Network text classifier, which achieves a state-of-the-art result by a significant margin. We empirically investigate several featurization methods to understand the conditions under which discourse features contribute non-trivial performance gains, and analyze discourse embeddings.
Anthology ID:
I17-1059
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
584–593
Language:
URL:
https://aclanthology.org/I17-1059
DOI:
Bibkey:
Cite (ACL):
Elisa Ferracane, Su Wang, and Raymond Mooney. 2017. Leveraging Discourse Information Effectively for Authorship Attribution. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 584–593, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Leveraging Discourse Information Effectively for Authorship Attribution (Ferracane et al., IJCNLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-2/I17-1059.pdf
Presentation:
 I17-1059.Presentation.pdf
Code
 elisaF/authorship-attribution-discourse