Fully Convolutional Networks for Text Classification
Abstract
In this work I propose a new way of using fully convolutional networks for classification while allowing for input of any size. I additionally propose two modifications on the idea of attention and the benefits and detriments of using the modifications. Finally, I show suboptimal results on the ITAmoji 2018 tweet to emoji task and provide a discussion about why that might be the case as well as a proposed fix to further improve results.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2019
- DOI:
- 10.48550/arXiv.1902.05575
- arXiv:
- arXiv:1902.05575
- Bibcode:
- 2019arXiv190205575A
- Keywords:
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- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- 6 pages, 4 tables, 3 figures, submitted for the EVALITA 2018 workshop as part of clic-it 2018 conference