Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings

Shubham Gondane


Abstract
This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.
Anthology ID:
W19-3218
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–113
Language:
URL:
https://www.aclweb.org/anthology/W19-3218
DOI:
10.18653/v1/W19-3218
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3218.pdf