@inproceedings{myers-palmer-2019-cleartac,
    title = "{C}lear{TAC}: Verb Tense, Aspect, and Form Classification Using Neural Nets",
    author = "Myers, Skatje  and
      Palmer, Martha",
    editor = "Xue, Nianwen  and
      Croft, William  and
      Hajic, Jan  and
      Huang, Chu-Ren  and
      Oepen, Stephan  and
      Palmer, Martha  and
      Pustejovksy, James",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-3315/",
    doi = "10.18653/v1/W19-3315",
    pages = "136--140",
    abstract = "This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset."
}