Biomedical Named Entity Recognition
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Biomedical named entity recognition (BioNER) can be challenging due to the lack of large-scale labeled training data and domain knowledge.
- In addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain.
- previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to the input word embeddings.
The paper proposed BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information.
- They evaluate the approach on six English biomedical datasets and it outperforms the strong baseline method.