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Ellipsis and Coreference Resolution in a Computerized Virtual Patient Dialogue System.

Chuan-Jie Lin1, Chien-Wei Pao2, Yen-Heng Chen2

  • 1Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. cjlin@mail.ntou.edu.tw.

Journal of Medical Systems
|August 13, 2016
PubMed
Summary

This study introduces a module for resolving ellipsis and coreference in virtual patient dialogues, improving natural language understanding in medical AI systems.

Keywords:
Computerized virtual patientCoreference resolutionDialogue systemEllipsisPronominal anaphora

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence in Healthcare
  • Computational Linguistics

Background:

  • Computerized virtual patient dialogue systems require sophisticated natural language understanding capabilities.
  • Accurate interpretation of medical dialogues is crucial for effective diagnosis and patient interaction.
  • Ellipsis and coreference present significant challenges in dialogue systems, hindering accurate information extraction.

Purpose of the Study:

  • To design and implement an ellipsis and coreference resolution module for a virtual patient dialogue system.
  • To enhance the natural language understanding capabilities of AI in medical diagnostic conversations.
  • To improve the accuracy of information retrieval from real medical diagnosis dialogues.

Main Methods:

  • Collection and analysis of real medical diagnosis dialogues.
  • Definition of diagnosis-related concept groups.
  • Development of rules, patterns, and features for ellipsis and coreference detection and resolution.

Main Results:

  • Achieved F-scores of 89.15% for ellipsis detection and 83.40% for resolution.
  • Reached F-scores of 93.83% for phrasal coreference detection and 83.40% for resolution.
  • Obtained high accuracy in pronominal anaphora resolution (92% for specific entities, 97.31% for others).

Conclusions:

  • The developed module effectively addresses ellipsis and coreference in medical dialogues.
  • The system demonstrates significant improvements in understanding complex linguistic phenomena within diagnostic conversations.
  • This work contributes to more robust and accurate virtual patient dialogue systems for medical applications.