BCH-NLP at BioCreative VII Track 3: medications detection in tweets using transformer networks and multi-task learning
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Abstract (arXiv)
In this paper, we present our work participating in the BioCreative VII Track 3 - automatic extraction of medication names in tweets, where we implemented a multi-task learning model that is jointly trained on text classification and sequence labelling. Our best system run achieved a strict F1 of 80.4, ranking first and more than 10 points higher than the average score of all participants. Our analyses show that the ensemble technique, multi-task learning, and data augmentation are all beneficial for medication detection in tweets.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 26 Nov 2021.
Builds on
5 of 12 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
- 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
- 2019Publicly Available Clinical BERT Embeddingscited by 1,002
- 2020COMETA: A Corpus for Medical Entity Linking in the Social Mediacited by 50
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.