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Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition
[article]
2020
arXiv
pre-print
Developing high-performing systems for detecting biomedical named entities has major implications. State-of-the-art deep-learning based solutions for entity recognition often require large annotated datasets, which is not available in the biomedical domain. Transfer learning and multi-task learning have been shown to improve performance for low-resource domains. However, the applications of these methods are relatively scarce in the biomedical domain, and a theoretical understanding of why
arXiv:2011.00425v1
fatcat:5tpxymmqfvanjm2pxmsscpphde