The ability construction of multi-scene knowledge generalization extraction in English context based on BERT

In order to study the construction of English knowledge graphs and efficiently process English information, this paper proposes a generalization knowledge extraction system based on BERT, which effectively extracts English information across multiple scenarios. Ablation experiments on three datasets and comparison experiments with SDP-LSTM and CNN classical models validate the effectiveness and superiority of the proposed BERT model. Experiments show that its unique structure can better learn effective semantic features, and on this basis, it integrates the three external features of keywords, entity information, and entity type. This minimizes the loss of critical information and further improves the knowledge extraction performance of BERT models. In the future, the knowledge extraction effect of this model in specific professional fields can be further explored, and the model can be optimized to produce practical value.