Biomedical NER using Novel Schema and Distant Supervision

Anshita Khandelwal, Alok Kar, Veera Raghavendra Chikka, Kamalakar Karlapalem


Abstract
Biomedical Named Entity Recognition (BMNER) is one of the most important tasks in the field of biomedical text mining. Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets. In this paper, we introduce a novel annotation schema to capture complex entities, and explore the effects of distant supervision on our deep-learning sequence labelling model. For BMNER task, our annotation schema outperforms other BIO-based annotation schemes on the same model. We also achieve higher F1-scores than state-of-the-art models on multiple corpora without fine-tuning embeddings, highlighting the efficacy of neural feature extraction using our model.
Anthology ID:
2022.bionlp-1.15
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
155–160
Language:
URL:
https://aclanthology.org/2022.bionlp-1.15
DOI:
10.18653/v1/2022.bionlp-1.15
Bibkey:
Cite (ACL):
Anshita Khandelwal, Alok Kar, Veera Raghavendra Chikka, and Kamalakar Karlapalem. 2022. Biomedical NER using Novel Schema and Distant Supervision. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 155–160, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Biomedical NER using Novel Schema and Distant Supervision (Khandelwal et al., BioNLP 2022)
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PDF:
https://aclanthology.org/2022.bionlp-1.15.pdf
Video:
 https://aclanthology.org/2022.bionlp-1.15.mp4