@inproceedings{pouran-ben-veyseh-etal-2019-graph,
    title = "Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures",
    author = "Pouran Ben Veyseh, Amir  and
      Nguyen, Thien Huu  and
      Dou, Dejing",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P19-1432/",
    doi = "10.18653/v1/P19-1432",
    pages = "4393--4399",
    abstract = "Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP."
}