12 results about "Language semantics" patented technology

The present application relates to a large model Lao translation method and system based on agent cooperation and cross-language semantic alignment, belonging to the field of natural language processing and machine translation. In the independent multilingual translation generation stage, the target Lao translation corresponding to the source sentence, the reference language translation and the linguistically similar language translation are generated respectively; cross-language semantic alignment and correction are carried out, the English translation is taken as the semantic alignment reference, the Thai translation provides morphological and syntactic guidance, and through the iterative information interaction between agents, the Lao translation is continuously evaluated and corrected; in the semantic clarification and strengthening stage, the key semantic fragments of the source sentence are extracted by analyzing the agent, the overall structure is simplified, and the Lao agent is assisted to generate the final high-fidelity translation result. The present application significantly improves the semantic accuracy and structural rationality of the Lao translation result; effectively solves the semantic illusion problem easily produced by the large language model in the Lao machine translation scene due to data scarcity.

The application relates to the technical field of speech recognition, and discloses a cross-modal attention fusion method and device based on voiceprint features and language semantics, which comprises the following steps: performing a feature extraction operation on speech data to obtain voiceprint features; based on a sentiment perception mask mechanism and a context sentiment memory unit, fusing sentiment features into text data to obtain semantic features; constructing a cross-modal relationship graph of the voiceprint features and the semantic features; determining a time sequence dependency relationship between the voiceprint features and the semantic features based on the edge weights generated by the cross-modal relationship graph; projecting the voiceprint features and the semantic features into the same semantic space based on the time sequence dependency relationship, and performing a feature reconstruction fusion operation based on a reconstruction loss function to obtain voiceprint and semantic fusion features; and adjusting a dialogue strategy determined based on the voiceprint and semantic fusion features; and using the adjusted dialogue strategy to generate interactive response data with historical interactive data, so that a high-accuracy and personalized speech interaction experience is realized.

The application discloses a building short-term load prediction method based on a lightweight language model, and relates to the field of building short-term load prediction.The application comprises the following steps: performing data preprocessing on historical multivariate time series data, dividing the processed historical multivariate time series data into multiple non-overlapping time segments, processing each time segment, outputting text embedding and numerical embedding, splicing and linearly aligning semantic embedding vectors and numerical embedding vectors, generating multivariate fusion embedding through a multivariate block feature fusion mechanism, and inputting the multivariate fusion embedding sequence into a frozen pre-trained large language model.The application effectively bridges the representation gap between the numerical time series mode and the language semantic space by converting timestamp information and statistical features into structured natural language descriptions and introducing a pre-trained language model for coding, so that the model can better understand the periodicity, trend and context dependence in the load data.

The application relates to the technical field of speech recognition, and discloses a cross-modal attention fusion method and device based on voiceprint features and language semantics, which comprises the following steps: performing a feature extraction operation on speech data to obtain voiceprint features; based on a sentiment perception mask mechanism and a context sentiment memory unit, fusing sentiment features into text data to obtain semantic features; constructing a cross-modal relationship graph of the voiceprint features and the semantic features; determining a time sequence dependency relationship between the voiceprint features and the semantic features based on the edge weights generated by the cross-modal relationship graph; projecting the voiceprint features and the semantic features into the same semantic space based on the time sequence dependency relationship, and performing a feature reconstruction fusion operation based on a reconstruction loss function to obtain voiceprint and semantic fusion features; and adjusting a dialogue strategy determined based on the voiceprint and semantic fusion features; and using the adjusted dialogue strategy to generate interactive response data with historical interactive data, so that a high-accuracy and personalized speech interaction experience is realized.

The application relates to the technical field of artificial intelligence, affective computing and computer-assisted teaching, in particular to a sign language emotion recognition and teaching feedback method based on machine learning, which collects learner sign language videos and extracts face, hand and posture holographic key points; action semantic features and emotion expression features are obtained in parallel by using a double-branch time sequence coding network; the sign language content and the emotion state containing continuous values of valence-arousal-dominance and discrete categories are obtained by a semantic recognition subnetwork and an emotion recognition subnetwork respectively; the recognition result is compared with standard semantics and emotion labels, and emotion intensity, naturalness, semantic matching degree scores and comprehensive quality scores are calculated; emotion correction instructions, reinforcement learning training sequence planning and key point trajectory visualization feedback are generated based on the scores; and teaching strategies are iteratively improved through closed-loop optimization and emotion resonance models. The application realizes synchronous recognition and quantitative evaluation of sign language semantics and emotions, and provides an adaptive feedback means for sign language emotion expression ability training.

The application provides a sign language generation method and device, electronic equipment and a storage medium. The method can obtain to-be-converted information, wherein the to-be-converted information includes text information and / or voice information. The semantic content of the to-be-converted information is determined, and the semantic content is converted into sign language semantic content with a semantic expression mode conforming to a sign language expression mode. Based on the sign language semantic content, a sign language action image is generated. In this way, the sign language action image can be automatically generated based on the to-be-converted information, and it is ensured that the hearing-impaired person can obtain effective external information.

This invention relates to the field of network content security technology and discloses a method for malicious semantic fusion and feature extraction for multilingual social networks, comprising the following steps: S1, multilingual corpus construction and preprocessing stage; S2, cross-language semantic encoding stage; S3, cross-layer malicious semantic fusion stage; S4, cluster feature purification stage; S5, model discrimination and iterative optimization stage. This invention overcomes the limitations of monolingual malicious content detection technology, realizes unified representation and extraction of cross-language malicious semantics in multilingual social network scenarios, effectively eliminates the surface and deep heterogeneity between different language data, can adapt to the high-frequency multilingual mixed text and low-resource language content processing needs in social scenarios, and significantly improves the discriminability and representation ability of cross-language malicious semantic features by directionally strengthening malicious semantic-related features through an adaptive feature fusion mechanism.

The application provides a cross-language text clustering method and system based on high-dimensional vector space manifold alignment, and relates to the technical field of natural language processing.The method comprises the following steps: constructing a source language and target language feature matrix through feature extraction, generating a pseudo-anchor point matrix by unsupervised topological matching, performing space centering processing, and executing orthogonal Procrustes analysis; based on the orthogonal transformation matrix, scaling factor and translation vector, performing rigid body transformation on the source language feature matrix, fusing the target language feature matrix, constructing a unified manifold space, performing clustering processing, and obtaining the semantic cluster division result of the cross-language text. Through the application, the technical problem of the prior art that the local topological structure of the source language semantic space is destroyed due to the dependence on large-scale parallel corpus for forced space mapping, which affects the cross-language text clustering accuracy and causes clustering drift can be solved, the topological coincidence of different language texts in a unified space is realized, and the cross-language text clustering accuracy is improved.

The application discloses a world model-based data transmission system and method, belonging to the field of data transmission, and through a first world model of a sending end, semantic decoupling and abstract coding are performed on input original multimedia data to generate a semantic-level state vector and a target identifier, a second world model of a receiving end retrieves and calls corresponding target identification features from a locally preset feature library, and the target identification features are taken as a rendering base to combine the semantic-level state vector and restore multimedia data containing the target object through generative reconstruction. Since the semantic-level state vector only includes one or more of environmental semantics, behavior semantics and language semantics and does not include target identification features, the amount of information sent by the sending end to the receiving end is reduced, high-quality audio and video reconstruction is still guaranteed under a low code rate, and efficient and faithful streaming media transmission is realized under limited channel bandwidth.

The application discloses a heterogeneous multi-modal remote sensing target detection method based on language pivot pre-training, constructs a pre-training framework containing a visual encoder and a large language model, uses a large language model as a semantic pivot through a concept sharing instruction alignment mechanism, maps visual features of heterogeneous sensors such as RGB, SAR and infrared to a shared language semantic space, and realizes implicit cross-modal alignment; meanwhile, a hierarchical visual-semantic annealing mechanism is introduced, multi-scale intermediate layer features of the visual encoder are gradually aggregated through time-dependent dynamic coefficients, and the granularity mismatch problem between language high-level semantics and detection task fine-grained features is solved. In the pre-training stage, the application decouples modal alignment and downstream task learning, and significantly improves the training stability, convergence speed and detection precision of the model on heterogeneous remote sensing data.

This invention relates to the field of report design and data processing technology, specifically a method and system for automatically generating report templates based on multi-stage semantic understanding. The method includes: generating a physical layout intermediate representation with percentage coordinates; constructing layout-aware prompts to guide a large language model to perform joint reasoning combining spatial relationships, visual styles, and linguistic semantics, identifying and matching label / field roles, and outputting a semantic layout intermediate representation; converting the semantic layout into a JRXML file that strictly follows the JasperReportsXML schema definition order based on coordinate mapping rules and a component mapping table; performing static compilation checks and repairs on the generated JRXML using an LLM; and establishing an LLM self-repairing closed loop with Jasper compiler errors as feedback, until a successfully compileable template is generated. This invention achieves end-to-end automated conversion from static PDFs to dynamic Jasper templates, significantly improving the efficiency and accuracy of report template development.