Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis
Laurent Vanni, Melanie Ducoffe, Carlos Aguilar, Frederic Precioso, Damon Mayaffre
Abstract
In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the efficiency of our Text Deconvolution Saliency on corpora from three different languages: English, French, and Latin. For every tested dataset, our Text Deconvolution Saliency automatically encodes complex linguistic patterns based on co-occurrences and possibly on grammatical and syntax analysis.- Anthology ID:
- P18-1051
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 548–557
- Language:
- URL:
- https://aclanthology.org/P18-1051
- DOI:
- 10.18653/v1/P18-1051
- Cite (ACL):
- Laurent Vanni, Melanie Ducoffe, Carlos Aguilar, Frederic Precioso, and Damon Mayaffre. 2018. Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 548–557, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis (Vanni et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1051.pdf