TY - JOUR
T1 - Transfer learning for biomedical named entity recognition with BioBert
AU - Symeonidou, Anthi
AU - Sazonau, Viachaslau
AU - Groth, Paul
N1 - Publisher Copyright:
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2019
Y1 - 2019
N2 - We apply a transfer learning approach to biomedical named entity recognition and compare it with traditional approaches (dictionary, CRF, BiLTSM). Specifically, we build models for adverse drug reaction recognition on three datasets. We tune a pre-trained transformer model, BioBERT, on these datasets and observe the absolute F1-score improvements of 6.93, 10.46 and 13.31. This shows that, with a relatively small amount of annotated data, transfer learning can help in specialized information extraction tasks.
AB - We apply a transfer learning approach to biomedical named entity recognition and compare it with traditional approaches (dictionary, CRF, BiLTSM). Specifically, we build models for adverse drug reaction recognition on three datasets. We tune a pre-trained transformer model, BioBERT, on these datasets and observe the absolute F1-score improvements of 6.93, 10.46 and 13.31. This shows that, with a relatively small amount of annotated data, transfer learning can help in specialized information extraction tasks.
KW - Adverse drug reaction
KW - BIO tagging
KW - BioBERT
KW - Drug safety
KW - Named entity recognition
KW - Text mining
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85072883722&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85072883722
SN - 1613-0073
VL - 2451
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 15th International Conference on Semantic Systems, SEMPDS 2019
Y2 - 9 September 2019 through 12 September 2019
ER -