@inproceedings{ning-etal-2018-improving,
    title = "Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource",
    author = "Ning, Qiang  and
      Wu, Hao  and
      Peng, Haoruo  and
      Roth, Dan",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/N18-1077/",
    doi = "10.18653/v1/N18-1077",
    pages = "841--851",
    abstract = "Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource {--} a probabilistic knowledge base acquired in the news domain {--} by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987{--}2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available."
}