@inproceedings{rao-etal-2017-biomedical,
title = "Biomedical Event Extraction using {A}bstract {M}eaning {R}epresentation",
author = "Rao, Sudha and
Marcu, Daniel and
Knight, Kevin and
Daum{\'e} III, Hal",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2315",
doi = "10.18653/v1/W17-2315",
pages = "126--135",
abstract = "We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results.",
}
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<abstract>We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results.</abstract>
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%0 Conference Proceedings
%T Biomedical Event Extraction using Abstract Meaning Representation
%A Rao, Sudha
%A Marcu, Daniel
%A Knight, Kevin
%A Daumé III, Hal
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F rao-etal-2017-biomedical
%X We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results.
%R 10.18653/v1/W17-2315
%U https://aclanthology.org/W17-2315
%U https://doi.org/10.18653/v1/W17-2315
%P 126-135
Markdown (Informal)
[Biomedical Event Extraction using Abstract Meaning Representation](https://aclanthology.org/W17-2315) (Rao et al., BioNLP 2017)
ACL