@InProceedings{srikumar:2017:Long,
  author    = {Srikumar, Vivek},
  title     = {An Algebra for Feature Extraction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1891--1900},
  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.},
  url       = {http://aclweb.org/anthology/P17-1173}
}