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
- 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)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-2315.pdf
- Data
- Bio