2 results about "Lexical semantics" patented technology

The invention discloses a court trial verification method based on a multi-modal Transform, and the method comprises the following steps: collecting multi-modal information data, and carrying out the preprocessing and feature extraction; inputting the data to a modal encoder for dimension adjustment and standardization processing; inputting to a fusion module of the TriMERT model to realize semantic alignment and feature fusion; inputting the data to a reasoning module, and carrying out multi-layer feature coding and context modeling; comparing with the vocabulary semantic features, generating verification information, and integrating and judging; calculating a modal contribution score, and performing feature weighting and key frame labeling on each modal; and integrating the verification judgment result and the reasoning path indication, and displaying in a structured report form. According to the method, a multi-mode Transform modeling method is adopted, court hearing semantic verification and reasoning path generation are achieved, and the method has the advantages of being high in fusion degree, accurate in recognition and high in interpretability.

The application relates to the technical field of text error correction, and discloses an automatic error correction method and system based on a knowledge graph. Based on the lexical semantic association and the syntax dependency rule of the knowledge graph, the surrounding fragments of potential errors of input text are extracted, and semantic similarity is calculated to obtain error association weight data; an influence ranking is generated in combination with the domain importance attribute of the error type, and a to-be-corrected error list is formed after context sentiment tendency analysis adjustment; global semantic representation vectors are constructed based on the knowledge graph, domain categories are recognized, and semantic constraint conditions are generated, a preliminary error correction path is screened, semantic fitness is calculated to determine an optimal path, text correction is completed in combination with a high-priority error correction rule, and a result is output. Through the method, semantic deviation can be effectively avoided, error correction accuracy and pertinence can be improved, the semantic integrity and expression coherence of the corrected text can be ensured, and the problem of insufficient adaptability of traditional error correction methods is solved.