@InProceedings{derczynski:2016:COLING,
  author    = {Derczynski, Leon},
  title     = {Representation and Learning of Temporal Relations},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1937--1948},
  abstract  = {Determining the relative order of events and times described in text is an
	important problem in natural language processing. It is also a difficult one:
	general state-of-the-art performance has been stuck at a relatively low ceiling
	for years. We investigate the representation of temporal relations, and
	empirically evaluate the effect that various temporal relation representations
	have on machine learning performance. While machine learning performance
	decreases with increased representational expressiveness, not all
	representation simplifications have equal impact.},
  url       = {http://aclweb.org/anthology/C16-1182}
}