ArticleBMC bioinformatics2023
Child-Sum EATree-LSTMs: enhanced attentive Child-Sum Tree-LSTMs for biomedical event extraction.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Effective type label-based synergistic representation learning for biomedical event trigger detection.BMC bioinformatics · 2024Article
- MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction.PloS one · 2024Article
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Authors and funding
5 authors.
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Abstract
backgroundTree-structured neural networks have shown promise in extracting lexical representations of sentence syntactic structures, particularly in the detection of event triggers using recursive neural networks.
methodsIn this study, we introduce an attention mechanism into Child-Sum Tree-LSTMs for the detection of biomedical event triggers. We incorporate previous researches on assigning attention weights to adjacent nodes and integrate this mechanism into Child-Sum Tree-LSTMs to improve the detection of event trigger words. We also address a limitation of shallow syntactic dependencies in Child-Sum Tree-LSTMs by integrating deep syntactic dependencies to enhance the effect of the attention mechanism.
resultsOur proposed model, which integrates an enhanced attention mechanism into Tree-LSTM, shows the best performance for the MLEE and BioNLP'09 datasets. Moreover, our model outperforms almost all complex event categories for the BioNLP'09/11/13 test set.
conclusionWe evaluate the performance of our proposed model with the MLEE and BioNLP datasets and demonstrate the advantage of an enhanced attention mechanism in detecting biomedical event trigger words.
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