Abstract
Identifying named entities and their interactions from biomedical texts is essential for medical research. In this work, we modify the graph neural networks for relation extraction (RIFRE) model for biomedical text, named Bio-RIFRE that jointly extracts chemical-gene named entities and their interactions from biomedical texts. This model (Bio-RIFRE) utilizes the heterogeneous graph neural network model to strengthen the representation of biomedical text and relation extraction. As a result, Bio-RIFRE achieves 3% better F1-score on the CHEMPROT dataset than the other joint entity-relation extraction models. In addition, The results show that in the overlapping cases, and when the number of relations in a sentence increases, the model is robust and achieves a better F1-score.
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This research is based upon work funded by the National Science Foundation under Grant No. 1625677.
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Esmail Zadeh Nojoo Kambar, M., Esmaeilzadeh, A., Taghva, K. (2022). Chemical-Gene Relation Extraction with Graph Neural Networks and BERT Encoder. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_17
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