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Do syntactic trees enhance Bidirectional Encoder Representations from Transformers (BERT) models for chemical-drug

Anfu Tang1,2, Louise Deléger1, Robert Bossy1

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Collecting chemical-drug relations is vital for biomedical research.
  • Pre-trained transformer models like BERT have limitations on biomedical texts due to scarce annotated data for relation extraction.
  • Syntactic information may enhance performance in chemical-drug relation extraction.

Purpose of the Study:

  • To investigate if enriching BioBERT with syntactic information improves chemical-drug relation extraction.
  • To propose and evaluate novel syntax-enhanced BioBERT models.

Main Methods:

  • Developed three syntax-enhanced BioBERT models: Chunking-Enhanced-BioBERT, Constituency-Tree-BioBERT, and Multi-Task-Syntactic (MTS)-BioBERT.
  • Tested an existing Late-Fusion model and ensemble systems combining syntax-enhanced and non-syntax-enhanced models.
  • Conducted experiments on the BioCreative VII DrugProt corpus.

Main Results:

  • Syntax-enhanced models generally degraded BioBERT's performance in biomedical relation extraction.
  • Performance improved when the subject-object distance of candidate semantic relations was long.
  • The impact of dependency parse quality on performance was explored.

Conclusions:

  • Syntactic enrichment offers conditional benefits for chemical-drug relation extraction, particularly for long-distance relations.
  • Further research is needed to optimize the integration of syntactic information into transformer models for biomedical text.
  • The study provides insights into the effectiveness of different syntax-enhancement strategies.