Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm.

OBJECTIVE

To address the challenges arising from the rapid growth of text data in the biomedical field, including the problems of irrelevant argument interference and deep semantic association neglect in existing event argument detection methods, as well as the difficulty of multiple event extraction. We aim to propose a new event argument detection method that can accurately mine valuable information from biomedical texts through multi-feature fusion and the question-and-answer paradigm, while also addressing the limitations of existing methods.

METHODS

We propose an event argument detection method based on multi-feature fusion and the question-answer paradigm. First, we split each event in the sentence into an independent question-and-answer format to reduce the complexity of detection. Then, in order to reduce the interference of irrelevant arguments, we use syntactic distance and external prior knowledge to find the corresponding argument prior template for each event type. Next, we introduce the multi-feature attention mechanism to fully explore the deep semantic features. Finally, we formulate post-processing methods for predefined event structures to form final biomedical events.

RESULTS

On the MLEE dataset, our model achieved 62.50% in event extraction of F1 scores, which is superior to other advanced event extraction methods.

CONCLUSION

Our method achieves good performance in the event extraction task and provides strong support for the mining of valuable information in biomedical texts.