@InProceedings{cotterell-EtAl:2018:N18-22,
  author    = {Cotterell, Ryan  and  Kirov, Christo  and  Mielke, Sebastian J.  and  Eisner, Jason},
  title     = {Unsupervised Disambiguation of Syncretism in Inflected Lexicons},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {548--553},
  abstract  = {Lexical ambiguity makes it difficult to compute useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token’s context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.},
  url       = {http://www.aclweb.org/anthology/N18-2087}
}