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Biomedical Named Entity Recognition

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Tags: Named Entity Recognition, Key-value memory networks, Nature Language Processing

This note is for Tian, Y., Shen, W., Song, Y., Xia, F., He, M., & Li, K. (2020). Improving biomedical named entity recognition with syntactic information. BMC Bioinformatics, 21(1), 539.

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.

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