@InProceedings{simova-uszkoreit:2017:RANLP,
  author    = {Simova, Iliana  and  Uszkoreit, Hans},
  title     = {Word Embeddings as Features for Supervised Coreference Resolution},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {686--693},
  abstract  = {A common reason for errors in coreference resolution is the lack of semantic
	information to help determine the compatibility between mentions referring to
	the same entity. Distributed representations, which have been shown successful
	in encoding relatedness between words, could potentially be a good source of
	such knowledge. Moreover, being obtained in an unsupervised manner, they could
	help address data sparsity issues in labeled training data at a small cost. In
	this work we investigate whether and to what extend features derived from word
	embeddings can be successfully used for supervised coreference resolution. We
	experiment with several word embedding models, and several different types of
	embeddingbased features, including embedding cluster and cosine
	similarity-based features. Our evaluations show improvements in the performance
	of a supervised state-of-theart coreference system.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_088}
}