@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature
%A Min, Bonan
%A Chan, Yee Seng
%A Qiu, Haoling
%A Fasching, Joshua
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F min-etal-2019-towards
%X 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.
%R 10.18653/v1/D19-1680
%U https://aclanthology.org/D19-1680
%U https://doi.org/10.18653/v1/D19-1680
%P 6444-6448
Markdown (Informal)
[Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature](https://aclanthology.org/D19-1680) (Min et al., EMNLP-IJCNLP 2019)
ACL