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ezTag: tagging biomedical concepts via interactive learning.

Dongseop Kwon1, Sun Kim2, Chih-Hsuan Wei2

  • 1School of Software Convergence, Myongji University, Seoul 03674, South Korea.

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|May 23, 2018
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Summary
This summary is machine-generated.

ezTag is a new web-based tool for biological concept annotation. It assists curators by providing automated pre-annotations and supports diverse concept types, even with limited training data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Text Mining

Background:

  • Advanced text-mining accelerates manual data curation through automated pre-annotations.
  • Current annotation systems are limited to common concepts (e.g., genes, diseases) due to scarce training data.

Purpose of the Study:

  • To develop a versatile web-based annotation tool, ezTag, for a wide range of biological concepts.
  • To enable annotation and training data generation with a human-in-the-loop approach, accommodating limited or no pre-existing training data.

Main Methods:

  • ezTag supports annotation of both PubMed abstracts and PubMed Central full-text articles.
  • It integrates lexicon-based tagging and state-of-the-art pre-trained taggers (TaggerOne, GNormPlus, tmVar).
  • The tool facilitates human-in-the-loop annotation for improved accuracy and data generation.

Main Results:

  • ezTag provides a flexible platform for annotating diverse biological entities.
  • It enhances the efficiency of data curation by offering multiple pre-annotation strategies.
  • The tool supports the creation of valuable training datasets for future text-mining models.

Conclusions:

  • ezTag addresses the limitations of current annotation systems by supporting a broader spectrum of biological concepts.
  • It empowers researchers to efficiently curate biological data and contribute to model training.
  • The freely available tool aims to advance biological text mining and data curation practices.