@InProceedings{vancranenburgh:2018:C18-2,
  author    = {van Cranenburgh, Andreas},
  title     = {Active DOP: an Active Learning Constituency Treebank Annotation Tool},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico},
  publisher = {Association for Computational Linguistics},
  pages     = {38--42},
  abstract  = {We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. Candidate analyses are generated by an exemplar-based parsing model that immediately learns from each new annotated sentence during annotation. This makes it suitable for situations in which only a limited seed treebank is available, or a radically different domain is being annotated. The tool offers the possibility to experiment with and evaluate active learning methods to speed up annotation in a naturalistic setting, i.e., measuring actual annotation costs and tracking specific user interactions. The code is made available under the GNU GPL license at https://github.com/andreasvc/activedop.},
  url       = {http://www.aclweb.org/anthology/C18-2009}
}