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Using distant supervision to augment manually annotated data for relation extraction

Author

Listed:
  • Peng Su
  • Gang Li
  • Cathy Wu
  • K Vijay-Shanker

Abstract

Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.

Suggested Citation

  • Peng Su & Gang Li & Cathy Wu & K Vijay-Shanker, 2019. "Using distant supervision to augment manually annotated data for relation extraction," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-17, July.
    • Handle: RePEc:plo:pone00:0216913
    DOI: 10.1371/journal.pone.0216913
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    References listed on IDEAS

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    1. Andre Lamurias & Luka A Clarke & Francisco M Couto, 2017. "Extracting microRNA-gene relations from biomedical literature using distant supervision," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-20, March.
    2. Domonkos Tikk & Philippe Thomas & Peter Palaga & Jörg Hakenberg & Ulf Leser, 2010. "A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-19, July.
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