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No means 'No': a non-improper modeling approach, with embedded speculative context.

Priya Tiwary1, Akshayraj Madhubalan1, Amit Gautam1

  • 1Saama AI Research Lab, Pune 411057, India.

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|August 30, 2022
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This study enhances bio-medical language models (BioBERT, BioELECTRA, PubMedBERT) to better understand negation and speculation in medical text. A novel super-tuning approach significantly improves their performance on key clinical NLP tasks.

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence in Medicine

Background:

  • Medical data contains complex terminology often appearing in varied contexts.
  • Existing bio-medical language models struggle to differentiate between negated and non-negated contexts.

Purpose of the Study:

  • To investigate the understanding of negation and speculation context in BioBERT, BioELECTRA, and PubMedBERT.
  • To introduce a generic super-tuning approach to enhance model embeddings for these contexts.

Main Methods:

  • Utilized cosine similarity scores of negated versus non-negated sentence embeddings to measure model understanding.
  • Developed a synthesized dataset for a generic super-tuning approach.
  • Fine-tuned super-tuned models on various downstream tasks.

Main Results:

  • Super-tuned models demonstrated improved understanding of negative and speculative contexts.
  • Achieved state-of-the-art performance on negation, speculation cue, and scope detection tasks using BioScope and Sherlock datasets.
  • Minimal performance trade-off observed in other tasks like natural language inference.

Conclusions:

  • The proposed super-tuning method effectively enhances bio-medical language models' ability to interpret negation and speculation.
  • This advancement is crucial for improving clinical Natural Language Processing (NLP) applications.
  • The enhanced models offer state-of-the-art performance on critical uncertainty detection tasks.