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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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Adaptable, high recall, event extraction system with minimal configuration.

Makoto Miwa, Sophia Ananiadou

    BMC Bioinformatics
    |July 24, 2015
    PubMed
    Summary

    This study presents an enhanced biomedical event extraction system, EventMine, that achieves high performance on new tasks with minimal tuning. The system uses configuration files and novel methods to adapt to different biomedical corpora, demonstrating its versatility and effectiveness.

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    Area of Science:

    • Biomedical Natural Language Processing (BioNLP)
    • Information Extraction
    • Computational Biology

    Background:

    • Biomedical event extraction is crucial in BioNLP research, with many existing systems requiring task-specific modifications.
    • A need exists for adaptable event extraction systems that perform well on new domains without extensive tuning.

    Purpose of the Study:

    • To enhance the EventMine system for improved adaptability and performance across diverse biomedical tasks.
    • To reduce the necessity for deep system modification and parameter tuning when applying to new corpora.

    Main Methods:

    • Integrated task-specific details into a configuration file.
    • Employed a weighting method and a covariate shift method to avoid extensive parameter tuning.
    • Applied the enhanced system to the Cancer Genetics and Pathway Curation sub-tasks of the BioNLP shared task 2013.

    Main Results:

    • EventMine achieved 1st place in the Pathway Curation task and 2nd place in the Cancer Genetics task.
    • The system demonstrated high recall in both sub-tasks with minimal task-specific configuration.
    • Further enhancements with covariate shift and entity generalization improved performance.

    Conclusions:

    • State-of-the-art event extraction systems can be applied to new tasks with high performance without internal modification.
    • Covariate shift and weighting methods effectively facilitate high recall and adapt models to target data.
    • These methods enable adaptation with minimal manual configuration and deep tuning.