@InProceedings{sagot-martinezalonso:2017:IWPT,
  author    = {Sagot, Beno\^{i}t  and  Mart\'{i}nez Alonso, H\'{e}ctor},
  title     = {Improving neural tagging with lexical information},
  booktitle = {Proceedings of the 15th International Conference on Parsing Technologies},
  month     = {September},
  year      = {2017},
  address   = {Pisa, Italy},
  publisher = {Association for Computational Linguistics},
  pages     = {25--31},
  abstract  = {Neural part-of-speech tagging has achieved competitive results with the
	incorporation of character-based and pre-trained word embeddings. In this
	paper, we show that a state-of-the-art bi-LSTM tagger can benefit from using
	information from morphosyntactic lexicons as additional input. The tagger,
	trained on several dozen languages, shows a consistent, average improvement
	when using lexical information, even when also using character-based
	embeddings, thus showing the complementarity of the different sources of
	lexical information. The improvements are particularly important for the
	smaller datasets.},
  url       = {http://www.aclweb.org/anthology/W17-6304}
}