Orthogonal Matching Pursuit for Text Classification
Konstantinos Skianis, Nikolaos Tziortziotis, Michalis Vazirgiannis
Abstract
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low sparsity. In this paper, we apply a greedy variable selection algorithm, called Orthogonal Matching Pursuit, for the text classification task. We also extend standard group OMP by introducing overlapping Group OMP to handle overlapping groups of features. Empirical analysis verifies that both OMP and overlapping GOMP constitute powerful regularizers, able to produce effective and very sparse models. Code and data are available online.- Anthology ID:
- W18-6113
- Volume:
- Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 93–103
- Language:
- URL:
- https://aclanthology.org/W18-6113
- DOI:
- 10.18653/v1/W18-6113
- Cite (ACL):
- Konstantinos Skianis, Nikolaos Tziortziotis, and Michalis Vazirgiannis. 2018. Orthogonal Matching Pursuit for Text Classification. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 93–103, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Orthogonal Matching Pursuit for Text Classification (Skianis et al., WNUT 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/W18-6113.pdf
- Code
- y3nk0/OMP-for-Text-Classification