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Domain Shift in Part-of-Speech Tagging.

Amila Kugic1, Stefan Schulz1, Markus Kreuzthaler1

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Domain shift significantly impacts machine learning in clinical natural language processing. Analyzing linguistic differences reveals variations in part-of-speech tags, necessitating domain adaptation for better performance.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Domain shift in dataset distributions poses challenges for machine learning models.
  • Clinical natural language processing (NLP) is particularly susceptible to performance degradation due to domain-specific linguistic variations.
  • Understanding these variations is crucial for developing robust NLP tools in healthcare.

Purpose of the Study:

  • To investigate the impact of domain shift on machine learning performance in clinical NLP.
  • To analyze linguistic differences across clinical narratives, biomedical abstracts, and news articles.
  • To identify specific linguistic features contributing to domain shift.

Main Methods:

  • Utilized part-of-speech (POS) tag distributions to quantify linguistic differences.
  • Compared POS tag frequencies across three distinct text domains: clinical narratives, biomedical abstracts, and news articles.
  • Employed statistical analysis to identify significant variations in tag occurrences.

Main Results:

  • Significant variations in part-of-speech tag distributions were observed across the analyzed domains.
  • Clinical datasets exhibited a higher frequency of undefined tags compared to biomedical abstracts and news articles.
  • These linguistic discrepancies highlight a notable domain shift impacting NLP model generalization.

Conclusions:

  • The study confirms substantial domain shift in clinical NLP datasets, affecting model accuracy.
  • The prevalence of undefined POS tags in clinical data underscores the need for domain-specific NLP solutions.
  • Improved domain adaptation techniques and specialized tools are essential for advancing clinical NLP performance.