An Insight Extraction System on BioMedical Literature with Deep Neural Networks

Hua He, Kris Ganjam, Navendu Jain, Jessica Lundin, Ryen White, Jimmy Lin


Abstract
Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.
Anthology ID:
D17-1285
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2691–2701
Language:
URL:
https://www.aclweb.org/anthology/D17-1285
DOI:
10.18653/v1/D17-1285
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1285.pdf