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@InProceedings{scheible16:_model_archit_quotat_detec,
  abstract = {Quotation detection is the task of locating spans of quoted speech in text. The state of the art treats this problem as a sequence labeling task and employs linear-chain conditional random fields. We question the efficacy of this choice: The Markov assumption in the model prohibits it from making joint decisions about the begin, end, and internal context of a quotation. We perform an extensive analysis with two new model architectures. We find that (a), simple boundary classification combined with a greedy prediction strategy is competitive with the state of the art; (b), a semi-Markov model significantly outperforms all others, by relaxing the Markov assumption.},
  address = {Berlin, Germany},
  author = {Scheible, Christian and Klinger, Roman and Pad{o}, Sebastian},
  booktitle = {Proceedings of ACL},
  interhash = {c77dfb02001fe26838c9936221ace71a},
  intrahash = {fbeab4234e533692e6d7e938fccff533},
  note = {Acceptance rate: 25  pages = {1736-1745},
  title = {Model Architectures for Quotation Detection},
  url = {https://www.aclweb.org/anthology/P/P16/P16-1164.pdf},
  year = 2016
}