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,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–135
Language:
URL:
https://www.aclweb.org/anthology/W17-2315
DOI:
10.18653/v1/W17-2315
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2315.pdf