@inproceedings{lin-etal-2017-representations,
    title = "Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks",
    author = "Lin, Chen  and
      Miller, Timothy  and
      Dligach, Dmitriy  and
      Bethard, Steven  and
      Savova, Guergana",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 16th {B}io{NLP} Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/W17-2341/",
    doi = "10.18653/v1/W17-2341",
    pages = "322--327",
    abstract = "Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task."
}