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A corpus to support eHealth Knowledge Discovery technologies.

Alejandro Piad-Morffis1, Yoan Gutiérrez2, Rafael Muñoz2

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This study introduces the eHealth-KD corpus, a Spanish dataset for semantic analysis in healthcare. It enables general knowledge extraction across various domains using machine learning.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Health Informatics

Background:

  • Developing general-purpose semantic frameworks for knowledge extraction is challenging.
  • Existing health-related corpora often rely on domain-specific labels, limiting broader applicability.
  • A need exists for a versatile corpus to capture semantic structures in health-related text.

Purpose of the Study:

  • To present and describe the eHealth-KD corpus, a novel Spanish health-related sentence collection.
  • To define a general semantic representation applicable across domains.
  • To provide baseline implementations for learning corpus semantics and facilitate future research.

Main Methods:

  • Manual annotation of 1173 Spanish health-related sentences with a general semantic structure.
  • Definition and illustration of the semantic representation with corpus examples.
  • Development of three baseline machine learning models to assess corpus complexity.

Main Results:

  • The eHealth-KD corpus comprises 1173 manually annotated Spanish health sentences.
  • A general semantic structure was defined and applied, avoiding domain-specific labels.
  • Baseline models demonstrated the feasibility of learning semantics from the corpus.

Conclusions:

  • The eHealth-KD corpus is a foundational resource for developing general-purpose semantic frameworks.
  • It facilitates knowledge extraction from diverse health-related text.
  • The corpus served as an evaluation scenario in TASS 2018, highlighting its utility.