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Improving biomedical named entity recognition by dynamic caching inter-sentence information.

Yiqi Tong1, Fuzhen Zhuang1,2, Huajie Zhang1

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This study introduces BioNER-Cache, a novel model that leverages inter-sentence context to improve biomedical named entity recognition (BioNER). The cache-based approach enhances accuracy by considering information beyond single sentences.

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Area of Science:

  • Biomedical Natural Language Processing
  • Bioinformatics
  • Computational Biology

Background:

  • Biomedical Named Entity Recognition (BioNER) identifies key entities like genes, chemicals, and diseases in text.
  • Current deep learning methods for BioNER often overlook inter-sentence context, leading to labeling inconsistencies.
  • Existing document-level BioNER studies highlight the importance of inter-sentence information, but its precise role remains unclear.

Purpose of the Study:

  • To address the limitations of sentence-level BioNER by incorporating inter-sentence context.
  • To develop a novel cache-based model, BioNER-Cache, for improved BioNER performance.
  • To explore the effective utilization of inter-sentence information in pre-training based BioNER models.

Main Methods:

  • A dynamic caching module captures inter-sentence information by storing recent hidden representations.
  • A query-and-read mechanism retrieves similar historical records from the cache to serve as local context.
  • An attention-based gated network integrates context-related features with BioBERT, employing a scoring function and multi-task learning for cache updates.

Main Results:

  • The proposed BioNER-Cache model demonstrates superior performance compared to state-of-the-art intra-sentence and inter-sentence baselines.
  • Extensive experiments on four biomedical datasets validate the effectiveness of the dynamic caching module.
  • The model successfully alleviates labeling inconsistency issues by incorporating broader contextual information.

Conclusions:

  • BioNER-Cache offers a significant advancement in biomedical named entity recognition by effectively utilizing inter-sentence context.
  • The dynamic caching mechanism provides a robust method for capturing and integrating relevant historical information.
  • This approach paves the way for more accurate and consistent BioNER systems in biomedical text processing.