@InProceedings{hardmeier:2016:COLING,
  author    = {Hardmeier, Christian},
  title     = {A Neural Model for Part-of-Speech Tagging in Historical Texts},
  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     = {922--931},
  abstract  = {Historical texts are challenging for natural language processing because they
	differ linguistically from modern texts and because of their lack of
	orthographical and grammatical standardisation. We use a character-level neural
	network to build a part-of-speech (POS) tagger that can process historical data
	directly without requiring a separate spelling normalisation stage. Its
	performance in a Swedish verb identification and a German POS tagging task
	is similar to that of a two-stage model. We analyse the performance of this
	tagger and a more traditional baseline system, discuss some of the remaining
	problems for
	tagging historical data and suggest how the flexibility of our neural tagger
	could be exploited to address diachronic divergences in morphology and syntax
	in early modern Swedish with the help of data from closely related languages.},
  url       = {http://aclweb.org/anthology/C16-1088}
}