ArticleBMC bioinformatics2018
Bidirectional long short-term memory with CRF for detecting biomedical event trigger in FastText semantic space.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Effective type label-based synergistic representation learning for biomedical event trigger detection.BMC bioinformatics · 2024Article
- BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.PLoS computational biology · 2020Article
- Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks.BMC bioinformatics · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
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6 authors.
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Abstract
backgroundIn biomedical information extraction, event extraction plays a crucial role. Biological events are used to describe the dynamic effects or relationships between biological entities such as proteins and genes. Event extraction is generally divided into trigger detection and argument recognition. The performance of trigger detection directly affects the results of the event extraction. In general, the traditional method is used to address the trigger detection as a classification task, as well as the use of machine learning or rules method, which construct many features to improve the classification results. Moreover, the classification model only recognizes triggers composed of single words, whereas for multiple words, the result is unsatisfactory.
resultsThe corpus of our model is MLEE. If we were to only use the biomedical LSTM and CRF model without other features, the F-score would reach about 78.08%. Comparing entity to part of speech (POS), we find the entity features more conducive to the improvement of performance of detection, with the F-score potentially reaching about 80%. Furthermore, we also experiment on the other three corpora (BioNLP 2009, BioNLP 2011, and BioNLP 2013) to verify the generalization of our model. Hence, F-scores can reach more than 60%, which are better than the comparative experiments.
conclusionsThe trigger recognition method based on the sequence annotation model does not require initial complex feature engineering, and only requires a simple labeling mechanism to complete the training. Therefore, generalization of our model is better compared to other traditional models. Secondly, this method can identify multi-word triggers, thereby improving the F-scores of trigger recognition. Thirdly, details on the entity have a crucial impact on trigger detection. Finally, the combination of character-level word embedding and word-level word embedding provides increasingly effective information for the model; therefore, it is a key to the success of the experiment.
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