8 results about "Semantic role labeling" patented technology

The application provides a sports intangible cultural heritage analysis and tracing method based on a knowledge graph, which comprises the following steps: according to an expert rule base, constructing entity metadata and cultural feature codes containing representative inheritors, typical actions and regional schools, combining with a historical influence index to quantify the initial weight of the entity, forming a structured semantic anchor point, embedding the semantic anchor point in the knowledge graph through an improved graph neural network, and performing multi-hop path tracking by fusing path semantic features; adopting a main and auxiliary dual-channel embedding optimization mechanism to enhance the traceability and semantic consistency of the path, introducing a multi-objective loss function in model training to improve the embedding quality and optimize the semantic alignment degree of path prediction and expert rules; based on the credibility evaluation of multi-hop semantic tracing and reasoning results of user queries, finally generating a visualized graph with semantic role labeling and branch interaction functions, and the application improves the structured expression, path interpretability and reasoning transparency of the knowledge graph.

The application provides a news synthesis method and system based on traditional algorithms and large models, and relates to the technical field of natural language processing and text generation. The method comprises: obtaining news texts of different data sources and preprocessing; using an improved text segmentation and sorting algorithm, performing semantic synthesis on the preprocessed news texts, generating a comprehensive news summary text and performing semantic role labeling to obtain a structured fact anchor point; constructing a structured prompt word template and configuring a low randomness parameter, and calling a large-scale language model to generate a preliminary news draft that meets the fact boundary according to the fact anchor point; performing quality detection on the news preliminary draft to obtain a comprehensive score report and a quality defect label of the news preliminary draft; using a large-scale language model to generate structured feedback of the news preliminary draft, and iteratively optimizing the news preliminary draft according to the feedback result to generate a news manuscript. The application realizes automatic generation of news with high accuracy, high consistency and clear structure.

This invention belongs to the field of natural language processing and knowledge graph technology, and discloses a method for entity association extraction and knowledge graph generation from scientific and technological documents based on natural language processing. The method includes: preprocessing the scientific and technological documents; extracting scientific and technological entities using a multimodal natural language processing model; and completing entity disambiguation by combining contextual semantics and a domain dictionary; identifying the association relationships between entities based on syntactic analysis and semantic role labeling; and calculating the association strength by introducing co-occurrence frequency, semantic similarity, and domain weight; constructing an initial knowledge graph; optimizing the graph structure through redundancy removal, conflict detection, and dynamic update mechanisms; and generating a scientific and technological document knowledge graph with high accuracy, completeness, and timeliness. This invention solves the problems of low accuracy in traditional scientific and technological document entity extraction, one-sided association identification, and static knowledge graphs, achieving efficient transformation from document text to structured knowledge, and providing strong support for scientific and technological intelligence analysis and scientific research innovation mining.

This invention discloses a multimodal retrieval method combining image features and semantic understanding, belonging to the field of information retrieval technology. This application employs a design that combines image segmentation technology with semantic role labeling, fully mining the semantic information in image and text data. By generating semantically anchored visual codebooks and cross-modal anchored indexes, deep fusion of images and text is achieved. This method can more accurately correspond and align visual and semantic information in images and text, alleviating the problems of information loss and low retrieval accuracy caused by independent processing of image and text data in existing technologies, significantly improving the accuracy and efficiency of multimodal retrieval. Simultaneously, by using anchor vectors from both the image and text sides for indexing, visual codewords and semantic role slots are tightly integrated, establishing more accurate cross-modal matching for matching and indexing between different modalities.

The application provides an old people comforting accompanying semantic understanding method and system based on natural language processing, relates to the technical field of natural language processing, and first collects old people's oral expressions and generates a complete semantic role framework through semantic role labeling enhancement processing; then constructs a sentiment dependency link to generate a sentiment semantic dependency set; then aligns the sentiment semantic dependency set with an old people high-frequency scene semantic library to generate a scene sentiment semantic adaptation model; generates a semantic sentiment linkage response text according to the scene sentiment semantic adaptation model; collects old people's feedback expressions, converts the feedback expressions into supplementary semantic units, adds the supplementary semantic units to the semantic library, adjusts the correlation degree calculation parameters, and re-trains the semantic role labeling enhancement algorithm. The application improves the understanding accuracy of old people's oral expressions, enhances the emotional resonance, and can provide more high-quality personalized old people comforting accompanying services.

This invention discloses a large-scale model-based answer analysis and management method and system, relating to the field of answer analysis technology. The large-scale model-based answer analysis and management system includes an answer analysis module and an answer review module. This invention constructs an answer-based spoken language mapping knowledge graph and combines it with the semantic role labeling capabilities of the large-scale model to achieve a mapping from surface text to deep semantic structures. This effectively solves the problems of non-standard and colloquial expressions in subjective answers, improving the system's understanding and tolerance for non-standard expressions. By introducing construction logic deduction paths from the expert question bank, non-standard answer words are decoupled into implicit practical action features and expected process purpose features. Similarity comparison is then performed based on engineering utility, elevating the judgment of answer results from keyword matching to engineering logic consistency judgment, significantly improving the professionalism and credibility of the judgment results.

The application discloses a multi-modal hate speech detection method based on rational enhancement dynamic anchor points, relates to the technical field of multi-modal content detection, and comprises the following steps: collecting multi-modal social platform data streams, extracting text and visual modal segments, and constructing a sample set; generating event triples by semantic role labeling and parsing the text, and establishing a cross-modal entity alignment mapping table in combination with the position of the visual face detection frame; extracting a text deep semantic vector and a visual area feature vector, and fusing to generate a multi-modal joint representation tensor; calculating the attention weight between modes by a dynamic anchor point generator, determining a rational enhancement dynamic anchor point set, and re-weighting the joint representation tensor to obtain a corrected feature matrix; and inputting the feature matrix into a pre-trained classification decision forest to output a hate speech detection result. The method realizes accurate alignment of cross-modal entities, dynamically optimizes feature weights, and improves the multi-modal hate speech detection effect.