A legal text semantic generation framework based on multimodal feature fusion and dynamic weight optimization

Due to the strict structured characteristics of legal provisions themselves, their semantics are highly dependent on explicit logical frameworks. Semantic analysis of them requires capturing implicit associations in the context. Therefore, the main difficulty of this study lies in the collaborative processing of multimodal features such as the structured provisions of legal provisions, the contextual relevance of precedents, and the implicit semantics of judicial interpretations. Traditional methods mainly focus on enhancing the logical coherence and thematic relevance of the generated text, while neglecting the integration of multimodal features of legal texts, resulting in poor semantic consistency between the semantic generation results and legal provisions or judicial documents. To this end, a legal text semantic generation framework based on multimodal feature fusion and dynamic weight optimization is proposed. The cross-modal noise in legal texts is filtered through regularization logic, and the Conditional Random Fields (CRF) domain adaptive word segmentation mechanism is selected to handle the ambiguity of professional term segmentation, achieving the preprocessing of legal texts. Introduce the attenuation factor of legal revision time and adopt the dynamic Term frequency-inverse Document Frequency (TF-IDF) Feature Engineering and Bidirectional Encoder Representations from Transformers for Legal EXpertise, BERT-LEX Obtain the weights of vocabulary in legal texts to extract the hierarchical semantic features of legal texts. By using the gated attention mechanism to dynamically embed the legal knowledge graph, a knowledge constraint generation model is constructed to enhance the legal consistency of the text and output the semantic generation results of the legal text. The big data set of real estate laws from 2020 to 2024 was selected for experimental verification. The results show that the ROUGE-L value, Legal BLEU value and Law Score value of the semantics of the legal texts generated by this method are all higher than 0.8, effectively enhancing the semantic coherence and legal logical accuracy.