Abstract
This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.- Anthology ID:
- E17-2043
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 267–273
- Language:
- URL:
- https://aclanthology.org/E17-2043
- DOI:
- Cite (ACL):
- Yunita Sari, Andreas Vlachos, and Mark Stevenson. 2017. Continuous N-gram Representations for Authorship Attribution. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 267–273, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Continuous N-gram Representations for Authorship Attribution (Sari et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/E17-2043.pdf