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Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger

Farrokh Mehryary1, Suwisa Kaewphan2, Kai Hakala1

  • 1Department of Information Technology, University of Turku, Turku, Finland ; The University of Turku Graduate School (UTUGS), University of Turku, Turku, Finland.

Journal of Biomedical Semantics
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This study introduces a novel method to filter false triggers in biomedical event extraction, significantly improving knowledge extraction quality. The approach enhances the accuracy of large-scale event databases like EVEX.

Keywords:
BioNLPEvent extractionTrigger detectionWord embeddings

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Area of Science:

  • Biomedical text mining
  • Bioinformatics
  • Natural Language Processing

Background:

  • Biomedical event extraction is crucial for database curation and hypothesis generation.
  • Trigger detection, identifying phrases for biological processes, is key to event extraction.
  • Existing systems require improved methods for filtering false triggers in large event databases.

Purpose of the Study:

  • To propose a novel approach for filtering falsely identified triggers from large-scale event databases.
  • To enhance the quality of knowledge extraction in biomedical text mining.
  • To improve the performance of state-of-the-art event extraction systems.

Main Methods:

  • Utilized state-of-the-art word embeddings and event statistics from biomedical literature.
  • Applied hierarchical clustering to frequent trigger words in the EVEX database.
  • Developed a supervised approach for rare trigger words, combining unsupervised clustering and manual annotation.

Main Results:

  • The method significantly improves the performance of event extraction systems.
  • Successfully removed 1,338,075 potentially incorrect events from the EVEX database.
  • Demonstrated applicability to any event extraction system or database, not limited to EVEX.

Conclusions:

  • The proposed method effectively filters false triggers, enhancing biomedical knowledge extraction.
  • This approach leads to substantial improvements in the quality and reliability of large-scale event databases.
  • The technique offers a valuable tool for advancing biomedical text mining applications.