Abstract
In this paper, we describe the system submitted for the shared task on Social Media Mining for Health Applications by the team Light. Previous works demonstrate that LSTMs have achieved remarkable performance in natural language processing tasks. We deploy an ensemble of two LSTM models. The first one is a pretrained language model appended with a classifier and takes words as input, while the second one is a LSTM model with an attention unit over it which takes character tri-gram as input. We call the ensemble of these two models: Neural-DrugNet. Our system ranks 2nd in the second shared task: Automatic classification of posts describing medication intake.- Anthology ID:
- W18-5912
- Volume:
- Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher, Abeed Sarker, Michael Paul
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 48–49
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/W18-5912/
- DOI:
- 10.18653/v1/W18-5912
- Cite (ACL):
- Nishant Nikhil and Shivansh Mundra. 2018. Neural DrugNet. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 48–49, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Neural DrugNet (Nikhil & Mundra, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/W18-5912.pdf