Explorers at #SMM4H 2023: Enhancing BERT for Health Applications through Knowledge and Model Fusion
This work addresses health information extraction from social media for researchers and practitioners, but it is incremental as it applies existing methods to a specific domain challenge.
The paper tackled health-related Named Entity Recognition (NER) from social media data by enhancing BERT through knowledge and model fusion, achieving first place in Task 3 of the #SMM4H 2023 Shared Tasks.
An increasing number of individuals are willing to post states and opinions in social media, which has become a valuable data resource for studying human health. Furthermore, social media has been a crucial research point for healthcare now. This paper outlines the methods in our participation in the #SMM4H 2023 Shared Tasks, including data preprocessing, continual pre-training and fine-tuned optimization strategies. Especially for the Named Entity Recognition (NER) task, we utilize the model architecture named W2NER that effectively enhances the model generalization ability. Our method achieved first place in the Task 3. This paper has been peer-reviewed and accepted for presentation at the #SMM4H 2023 Workshop.