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Active Learning of Regular Expressions for Entity Extraction.

Alberto Bartoli, Andrea De Lorenzo, Eric Medvet

    IEEE Transactions on Cybernetics
    |March 31, 2017
    PubMed
    Summary
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    This study introduces an active learning method to automatically create entity extraction rules (regular expressions) with minimal user input. It significantly reduces annotation effort and computational cost for text data processing.

    Area of Science:

    • Natural Language Processing
    • Machine Learning
    • Computational Linguistics

    Background:

    • Automatic entity extraction from unstructured text is challenging.
    • Existing methods require extensive user-annotated datasets.
    • Minimizing user annotation effort is crucial for practical applications.

    Purpose of the Study:

    • To develop an active learning approach for synthesizing entity extractors (regular expressions).
    • To significantly reduce the user annotation effort required for training.
    • To improve the efficiency of entity extraction system development.

    Main Methods:

    • Utilizing genetic programming (GP) to construct candidate regular expression solutions.
    • Employing a querying-by-committee strategy to select informative user queries.

    Related Experiment Videos

  • Integrating active learning to iteratively refine the extractor based on minimal user feedback.

    • Main Results:

    • Achieved high accuracy in entity extraction.
    • Demonstrated significant savings in computational effort and annotated data.
    • Outperformed a state-of-the-art baseline in terms of efficiency and user effort.

    Conclusions:

    • The proposed active learning approach effectively minimizes user annotation for entity extractor synthesis.
    • Genetic programming combined with querying-by-committee offers an efficient method for this task.
    • This approach provides a practical solution for developing entity extraction systems with reduced human supervision.