@InProceedings{mukherjee-kubler-scheutz:2017:EACLlong,
  author    = {Mukherjee, Atreyee  and  K\"{u}bler, Sandra  and  Scheutz, Matthias},
  title     = {Creating POS Tagging and Dependency Parsing Experts via Topic Modeling},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {347--355},
  abstract  = {Part of speech (POS) taggers and dependency parsers tend to work well on
	homogeneous datasets but their performance suffers on datasets containing data
	from different genres. In our current work, we investigate how to create POS
	tagging and dependency parsing experts for heterogeneous data by employing
	topic modeling. We create topic models (using Latent Dirichlet Allocation) to
	determine genres from a heterogeneous dataset and then train an expert for each
	of the genres. Our results show that the topic modeling experts reach
	substantial improvements when compared to the general versions. For dependency
	parsing, the improvement reaches 2 percent points over the full training
	baseline when we use two topics.},
  url       = {http://www.aclweb.org/anthology/E17-1033}
}