Papers › Biomedical Named Entity Recognition with Multilingual BERT

Biomedical Named Entity Recognition with Multilingual BERT

1 Nov 2019WS 2019 11archive 2025-07-28

Kai Hakala, Sampo Pyysalo

We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish biomedical named entity recognition. We apply a CRF-based baseline approach and multilingual BERT to the task, achieving an F-score of 88{\%} on the development data and 87{\%} on the test set with BERT. Our approach reflects a straightforward application of a state-of-the-art multilingual model that is not specifically tailored to either the language nor the application domain. The source code is available at: https://github.com/chaanim/pharmaconer

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Tasks

Named Entity Recognition Named Entity Recognition (NER)named-entity-recognition

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Test Weight Decay WordPiece

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