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Rémi Cardon1, Natalia Grabar1

  • 1UMR CNRS 8163 - STL, F-59000 Lille, France.

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|June 24, 2020
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Summary
This summary is machine-generated.

This study introduces a method to automatically identify parallel sentences in French biomedical texts. This resource aids in simplifying complex medical information for better patient understanding.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing

Background:

  • Parallel sentences convey similar information with variations in aspects like language or register.
  • Register variation in parallel sentences, such as expert vs. non-expert texts, is key for automatic text simplification.
  • Simplifying biomedical texts is crucial for patient comprehension, but resources are scarce.

Purpose of the Study:

  • To develop a method for detecting and aligning parallel sentences from comparable corpora in the biomedical domain.
  • To address the lack of resources for automatic text simplification in French biomedical literature.

Main Methods:

  • Utilizing comparable corpora distinguished by register (specialized and simplified versions).
  • Treating the detection and alignment of parallel sentences as a binary classification task.
  • Applying the method to French biomedical corpora.

Main Results:

  • Successfully demonstrated the ability to automatically generate a corpus of parallel sentences.
  • Validated the proposed method for identifying register-varied parallel sentences.

Conclusions:

  • The presented method effectively generates parallel sentence corpora from comparable texts.
  • This work facilitates the creation of resources for biomedical text simplification.