@InProceedings{shen-EtAl:2016:COLING1,
  author    = {Shen, Qinlan  and  Clothiaux, Daniel  and  Tagtow, Emily  and  Littell, Patrick  and  Dyer, Chris},
  title     = {The Role of Context in Neural Morphological Disambiguation},
  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     = {181--191},
  abstract  = {Languages with rich morphology often introduce sparsity in language processing
	tasks. While morphological analyzers can reduce this sparsity by providing
	morpheme-level analyses for words, they will often introduce ambiguity by
	returning multiple analyses for the same surface form. The problem of
	disambiguating between these morphological parses is further complicated by the
	fact that a correct parse for a word is not only be dependent on the surface
	form but also on other words in its context. In this paper, we present a
	language-agnostic approach to morphological disambiguation. We address the
	problem of using context in morphological disambiguation by presenting several
	LSTM-based neural architectures that encode long-range surface-level and
	analysis-level contextual dependencies. We applied our approach to Turkish,
	Russian, and Arabic to compare effectiveness across languages, matching
	state-of-the-art results in two of the three languages. Our results also
	demonstrate that while context plays a role in learning how to disambiguate,
	the type and amount of context needed varies between languages.},
  url       = {http://aclweb.org/anthology/C16-1018}
}