@InProceedings{zate-EtAl:2018:K18-2,
  author    = {{\"{O}}zate{\c{s}}, {\c{S}}aziye Bet{\"{u}}l  and  {\"{O}}zg{\"{u}}r, Arzucan  and  Gungor, Tunga  and  {\"{O}}zt{\"{u}}rk, Balkız},
  title     = {A Morphology-Based Representation Model for {LSTM}-Based Dependency Parsing of Agglutinative Languages},
  booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {238--247},
  abstract  = {We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.},
  url       = {http://www.aclweb.org/anthology/K18-2024}
}