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Generative Biomedical Event Extraction With Constrained Decoding Strategy
IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 14, 2024
Summary
This study introduces a new generative model for biomedical event extraction, outperforming existing methods. The novel approach uses a T5-based framework with constrained decoding and curriculum learning for more accurate event identification.
Area of Science:
- Computational biomedicine
- Bioinformatics
- Natural Language Processing
Background:
- Biomedical event extraction is crucial in computational biology and NLP.
- Existing extraction models face challenges due to cascading errors from sequential subtask processing.
Purpose of the Study:
- To develop a novel generative model for biomedical event extraction.
- To address the limitations of traditional extraction-based approaches.
Main Methods:
- A sequence-to-sequence generation paradigm based on the T5 pre-trained language model.
- Utilized constrained decoding for guided sequence generation.
- Employed curriculum learning for efficient model training.
Main Results:
- The proposed generative model achieved superior performance on the Genia 2011 and Genia 2013 benchmark datasets.
- Demonstrated the effectiveness of a generative approach over extraction-based methods.
Conclusions:
- Generative modeling offers a promising alternative for biomedical event extraction.
- The T5-based model with constrained decoding and curriculum learning enhances accuracy and efficiency.
