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Developing a disease outbreak event corpus.

Mike Conway1, Ai Kawazoe, Hutchatai Chanlekha

  • 1National Institute of Informatics, Tokyo, Japan. mike@nii.ac.jp

Journal of Medical Internet Research
|September 30, 2010
PubMed
Summary
This summary is machine-generated.

Researchers created a gold standard dataset and annotation scheme for evaluating disease outbreak information extraction systems. This resource aids in accurately identifying disease events from news texts, promoting open evaluation in the field.

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Area of Science:

  • Natural Language Processing
  • Public Health Informatics
  • Computational Linguistics

Background:

  • Growing use of information extraction for disease outbreak tracking from online news.
  • Lack of standardized evaluation resources for this emerging research area.

Purpose of the Study:

  • Develop a "gold standard" dataset for evaluating disease outbreak information extraction systems.
  • Provide an annotation scheme and corpus to encourage open evaluation and benchmarking.
  • Improve accuracy in identifying the semantics of disease outbreak events.

Main Methods:

  • Developed a detailed annotation scheme for identifying infectious disease outbreak events in news texts.
  • Defined an event to minimally include geographical and disease name information.
  • Allowed for rich encoding of related concepts like international travel and food contamination.

Main Results:

  • Created a 200-document corpus with 394 annotated disease outbreak events.
  • The corpus serves as a benchmark for evaluating event detection algorithms, including the BioCaster system.
  • Provided a download script and GUI software for corpus exploration.

Conclusions:

  • Presented a novel annotation scheme and corpus for evaluating disease outbreak event extraction algorithms.
  • The resources cater to the specific needs of systems like BioCaster and the broader research community.
  • Facilitates standardized evaluation in the growing field of public health surveillance using online news.