Causal event triplets play a pivotal role in understanding logical links between events. The research combined pattern methods with deep learning to address the lack of high-quality data sets and limited coverage of causal knowledge in extracting causal event triplets from texts. Firstly,lexical-syntactic patterns,reflecting causal rela-tionships,are created and matched within the Web corpus. Secondly,inverse document frequency and causal event boundary word strategies filter noise from the pattern matches. Then,rule-based normalisation of causal events fol-low,resulting in a high-quality causal event triplet dataset. Finally,in the bidirectional long short-term memory-conditional random fields ( BiLSTM-CRF ) model,characters,words,parts of speech,causal pattern feature words,and causal event boundary words are effectively integrated,along with the introduction of deep learning strategies. After training on the causal event triple dataset,the model performs well in extracting causal event tri-ples from a large-scale web corpus covering broad domain knowledge. Experimental results show that the causal event triplets F1 score is 92.44％ and boundary word identification precision is 94.00％. These findings validate the efficient integration of patterns with deep learning,the high quality of the dataset,and the method' s significant value in extracting causal event triplets from the Web corpus.
causal event triples/lexical-syntactic pattern/bidirectional long short-term memory-conditional random field ( BiLSTM-CRF)/multi-feature fusion/deep learning
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