@inproceedings{min-etal-2019-towards,
    title = "Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature",
    author = "Min, Bonan  and
      Chan, Yee Seng  and
      Qiu, Haoling  and
      Fasching, Joshua",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D19-1680/",
    doi = "10.18653/v1/D19-1680",
    pages = "6444--6448",
    abstract = "Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences. Towards achieving this grand goal, a crucial first step is to extract past interventions and when and where they have been applied, from hundreds of thousands of reports automatically. In this paper, we developed a corpus annotated with interventions to foster research, and developed an information extraction system for extracting interventions and their location and time from text. We demonstrate early, very encouraging results on extracting interventions."
}