Leveraging Discourse Information Effectively for Authorship Attribution
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:
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/I17-1059.pdf
- Code
- elisaF/authorship-attribution-discourse