@InProceedings{akbik-li:2016:COLING,
  author    = {Akbik, Alan  and  Li, Yunyao},
  title     = {K-SRL: Instance-based Learning for Semantic Role Labeling},
  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     = {599--608},
  abstract  = {Semantic role labeling (SRL) is the task of identifying and labeling
	predicate-argument structures in sentences with semantic frame and role labels.
	A known challenge in SRL is the large number of low-frequency exceptions in
	training data, which are highly context-specific and difficult to generalize.
	To overcome this challenge, we propose the use of instance-based learning that
	performs no explicit generalization, but rather extrapolates predictions from
	the most similar instances in the training data. We present a variant of
	k-nearest neighbors (kNN) classification with composite features to identify
	nearest neighbors for SRL. We show that high-quality predictions can be derived
	from a very small number of similar instances. In a comparative evaluation we
	experimentally demonstrate that our instance-based learning approach
	significantly outperforms current state-of-the-art systems on both in-domain
	and out-of-domain data, reaching F1-scores of 89,28% and 79.91% respectively.},
  url       = {http://aclweb.org/anthology/C16-1058}
}