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
- Editors:
- Davy Weissenbacher, Graciela Gonzalez-Hernandez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 110–113
- Language:
- URL:
- https://aclanthology.org/W19-3218
- DOI:
- 10.18653/v1/W19-3218
- Cite (ACL):
- Shubham Gondane. 2019. Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 110–113, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings (Gondane, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-3218.pdf
- Data
- SMM4H