Financial event extraction, as a key task in natural language processing, aims to extract structured event information from unstructured text, which is of great significance for financial market analysis and decision-making. Traditional pattern matching or machine learning methods have problems such as insufficient generalization ability and inadequate understanding of sentence and meaning. This article proposes two document level financial event extraction models: the PTPCG-DSGAT model based on syntactic dependency enhancement, which encodes text using BERT and combines the dependency syntactic enhancement module with the GAT semantic enhancement module to enhance entity semantic representation; An improved model based on the BKP algorithm introduces RoBERTa to capture multi-level semantic information, optimizes the construction of the pruned complete graph by combining importance calculation, and uses non autoregressive BKP decoding algorithm to improve the decoding efficiency of parameter combination. Experiments have shown that the PTPCG-DSGAT model significantly improves the accuracy of event extraction through syntactic semantic joint modeling, while the improved BKP decoding model performs better in pseudo trigger word screening and complex relationship decoding, with decoding speed and accuracy superior to the baseline method. Syntax dependency modeling and semantic enhancement techniques effectively alleviate the problem of semantic fragmentation in document level event extraction; The pruning strategy based on importance calculation significantly improves the quality of pseudo trigger words, thereby optimizing the event graph structure; The non autoregressive BKP algorithm optimizes the pivot path to reduce computational complexity while ensuring decoding accuracy. The research has verified the efficiency and robustness of the model on financial texts, providing technical reference for cross domain event extraction. In the future, further exploration of the model's generalization ability and interpretability is needed to adapt to complex scenarios in multiple fields.

Ying Liu. Research on Financial Event Extraction Model Based on Dependency Syntax and BKP Algorithm. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 96-105. https://doi.org/10.38007/SESR.2025.060110.

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