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BioPREP: Deep learning-based predicate classification with SemMedDB.

Gibong Hong1, Yuheun Kim1, YeonJung Choi1

  • 1Department of Digital Analytics, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.

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|August 19, 2021
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Summary
This summary is machine-generated.

This study introduces BioPREP, a large-scale dataset for biomedical relation extraction, and evaluates neural network models. BioBERT-based models achieved the highest accuracy in predicate classification, improving biomedical information extraction.

Keywords:
Neural networksPredicate datasetPretrained modelRelation extractionSemantic clustering

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Relation Extraction (RE) is crucial for biomedical information extraction, but traditional methods struggle with efficiency and accuracy.
  • Existing neural network approaches for RE often lack in-depth analysis of predicate accuracy variations and structured datasets for predicate classification.

Purpose of the Study:

  • To construct a large-scale biomedical relation extraction dataset (BioPREP) using SemMedDB and PKDE4J.
  • To evaluate the performance of various neural network-based algorithms for predicate classification on this dataset.
  • To analyze model performance by grouping predicates into semantic clusters.

Main Methods:

  • Creation of the Biomedical Predicate Relation-extraction with Entity-filtering by PKDE4J (BioPREP) dataset.
  • Evaluation of neural network models, including BioBERT and SciBERT, for relation extraction.
  • In-depth performance analysis through semantic clustering of predicates.

Main Results:

  • The BioBERT-based model achieved a high f1-score of 0.846 for predicate classification.
  • SciBERT-based models also showed strong performance with an f1-score of 0.840.
  • Semantic cluster analysis revealed improved classification for sentences with key phrases like 'comparison verb + than'.

Conclusions:

  • BioBERT demonstrates superior performance in biomedical predicate classification compared to other evaluated neural network models.
  • The BioPREP dataset and semantic clustering analysis provide valuable insights for improving biomedical relation extraction.
  • The findings highlight the potential of advanced NLP models for enhancing the extraction of relationships from biomedical literature.