%0 Conference Proceedings %T An Algebra for Feature Extraction %A Srikumar, Vivek %Y Barzilay, Regina %Y Kan, Min-Yen %S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2017 %8 July %I Association for Computational Linguistics %C Vancouver, Canada %F srikumar-2017-algebra %X 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. %R 10.18653/v1/P17-1173 %U https://aclanthology.org/P17-1173 %U https://doi.org/10.18653/v1/P17-1173 %P 1891-1900