Biomedical Event Extraction using Abstract Meaning Representation

Sudha Rao, Daniel Marcu, Kevin Knight, Hal Daumé III


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.
Anthology ID:
W17-2315
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Editors:
Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–135
Language:
URL:
https://aclanthology.org/W17-2315
DOI:
10.18653/v1/W17-2315
Bibkey:
Cite (ACL):
Sudha Rao, Daniel Marcu, Kevin Knight, and Hal Daumé III. 2017. Biomedical Event Extraction using Abstract Meaning Representation. In BioNLP 2017, pages 126–135, Vancouver, Canada,. Association for Computational Linguistics.
Cite (Informal):
Biomedical Event Extraction using Abstract Meaning Representation (Rao et al., BioNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-2315.pdf
Data
Bio