@InProceedings{plank:2016:WNUT,
  author    = {Plank, Barbara},
  title     = {Processing non-canonical or noisy text: fortuitous data to the rescue},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1},
  abstract  = {Real world data differs radically from the benchmark corpora we use in NLP,
	resulting in large performance drops. The reason for this problem is obvious:
	NLP models are trained on limited samples from canonical varieties considered
	standard. However, there are many dimensions, e.g., sociodemographic, language,
	genre, sentence type, etc. on which texts can differ from the standard. The
	solution is not obvious: we cannot control for all factors, and it is not clear
	how to best go beyond the current practice of training on homogeneous data from
	a single domain and language.  
	In this talk, I review the notion of canonicity, and how it shapes our
	community's approach to language. I argue for the use of fortuitous data.
	Fortuitous data is data out there that just waits to be harvested. It includes
	data which is in plain sight, but is often neglected, and more distant sources
	like behavioral data, which first need to be refined. They provide additional
	contexts and a myriad of opportunities to build more adaptive language
	technology, some of which I will explore in this talk.},
  url       = {http://aclweb.org/anthology/W16-3901}
}