Abstract
Though feature extraction is a necessary first step in statistical NLP, it is often seen as a mere preprocessing step. Yet, it can dominate computation time, both during training, and especially at deployment. In this paper, we formalize feature extraction from an algebraic perspective. Our formalization allows us to define a message passing algorithm that can restructure feature templates to be more computationally efficient. We show via experiments on text chunking and relation extraction that this restructuring does indeed speed up feature extraction in practice by reducing redundant computation.- Anthology ID:
- P17-1173
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1891–1900
- Language:
- URL:
- https://aclanthology.org/P17-1173
- DOI:
- 10.18653/v1/P17-1173
- Cite (ACL):
- Vivek Srikumar. 2017. An Algebra for Feature Extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1891–1900, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- An Algebra for Feature Extraction (Srikumar, ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P17-1173.pdf