@InProceedings{beck-cohn:2017:I17-2,
  author    = {Beck, Daniel  and  Cohn, Trevor},
  title     = {Learning Kernels over Strings using Gaussian Processes},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {67--73},
  abstract  = {Non-contiguous word sequences are widely known to be important in modelling
	natural language. However they not explicitly encoded in common text
	representations. In this work we propose a model for text processing using
	string kernels, capable of flexibly representing non-contiguous sequences.
	Specifically, we derive a vectorised version of the string kernel algorithm and
	their gradients, allowing efficient hyperparameter optimisation as part of a
	Gaussian Process framework. Experiments on synthetic data and text regression
	for emotion analysis show the promise of this technique.},
  url       = {http://www.aclweb.org/anthology/I17-2012}
}