14 results about "Comprehension approach" patented technology

The invention discloses a multi-agent brain signal autonomous understanding method based on large language model driving, and the method comprises the steps: constructing a hierarchical cooperation architecture comprising a central supervisor agent and a specialized sub-agent, and carrying out the autonomous understanding of a brain signal on the basis of reducing the technical threshold of brain signal analysis; the problems that a traditional normal form process is rigid and long-time-history complex tasks are difficult to process are solved. According to the method, a central supervisor agent is used for analyzing a natural language intention of a user and dynamically decomposing a task, and a specialized sub-agent is combined with a global sharing state and a context isolation mechanism to execute domain-specific full-link dynamic planning and accurate tool calling; a comprehensive analysis report with cross-domain causal logic is generated by integrating quantitative calculation results and qualitative clinical knowledge through hierarchical resource allocation and retrieval enhancement generation mechanism, then a three-layer difficulty assessment reference system is established to verify framework performance, and finally autonomy, flexibility and clinical interpretability of a brain signal understanding process are achieved.

The invention discloses a semantic understanding method and engine based on multi-model collaborative reasoning and self-supervised learning, and belongs to the technical field of natural language processing and artificial intelligence. The method comprises the following steps: preprocessing input data to extract semantic features; at least two models are intelligently scheduled from a large language model, a task type model and a rule engine for collaborative reasoning through a dynamic routing module based on a multi-level strategy and a meta-learning network; performing hierarchical fusion and conflict resolution on the reasoning result by using a multi-model fusion module, and outputting a final semantic understanding result; and continuous optimization of the system is realized through feedback learning. Universal semantic representation is learned by using unlabeled data through a self-supervised pre-training task, so that the generalization ability of the model is enhanced. The engine comprises an input processing core module, a dynamic routing core module, a multi-model fusion core module, a feedback learning core module, a model warehouse core module, a monitoring security core module and the like so as to collaboratively realize the method. According to the method, high-precision and high-efficiency semantic understanding with self-adaptive capability is realized.

The application discloses a text reading comprehension method and device based on article difference perception representation, a storage medium and an electronic equipment, belongs to the field of natural language processing and artificial intelligence, and aims to solve the technical problems of how to effectively utilize article information to improve the accuracy of answer selection and how to realize effective matching between a question and options, thereby improving the prediction accuracy of a text reading comprehension system, and adopts the technical scheme that: ① a text reading comprehension method based on article difference perception representation comprises the following modules: a pre-training embedding representation module, a feature filtering module, an article difference perception representation interaction module and a label prediction module. ② a text reading comprehension device based on article difference perception representation comprises a text reading comprehension data set acquisition unit, a text reading comprehension model construction unit and a text reading comprehension model training unit.

This invention discloses a semantic understanding method and engine based on multi-model collaborative reasoning and self-supervised learning, belonging to the fields of natural language processing and artificial intelligence. The method includes preprocessing input data to extract semantic features; intelligently scheduling at least two models from a large language model, task-oriented model, and rule engine for collaborative reasoning through a dynamic routing module based on multi-level policies and meta-learning networks; using a multi-model fusion module to perform hierarchical fusion and conflict resolution of the reasoning results, outputting the final semantic understanding result; and continuously optimizing the system through feedback learning. A self-supervised pre-training task is used to learn a general semantic representation from unlabeled data to enhance the model's generalization ability. The engine includes core modules such as input processing, dynamic routing, multi-model fusion, feedback learning, model repository, and monitoring and security, which work together to implement the above method. This invention achieves high-precision, high-efficiency, and adaptive semantic understanding.

The application discloses a text understanding method and system based on parallel multi-dimensional semantic signal output, and belongs to the technical field of natural language processing. The method simultaneously performs parallel analysis on literal dimension, intention dimension and emotion dimension of input text, and fuses signals of the three dimensions into comprehensive cognitive signal output. The intention dimension adopts sentence structure recognition and does not need to depend on a large-scale language model. The method simulates the instantaneous understanding process of the human brain through a parallel multi-dimensional architecture, and outputs cognitive signals that can be directly used by the brain, rather than fragmented labels.

The application discloses an AI robot dialogue understanding method based on causal reasoning, comprising the following steps: obtaining user dialogue text, and splitting semantic roles, event elements, emotional expressions and key sentences into semantic microparticles; establishing a semantic charge conservation constraint in a semantic microparticle set, and adaptively adjusting microparticle charges; generating causal bias change information for the semantic microparticles, and updating the moving trend of the microparticles in the semantic space; constructing a semantic microparticle density field, identifying a semantic area with a density exceeding a threshold, and determining the semantic meaning corresponding to the area as the user intent of the current dialogue; and generating a robot action instruction or a natural language reply matched with the user intent and the moving trend of the semantic microparticles in the area. The application realizes fine-grained semantic tracking and stable robot response generation in multi-round dialogue by splitting dialogue text into semantic microparticles and applying a semantic charge conservation and causal bias driving mechanism.

The application provides a multi-scene semantic understanding method and device, electronic equipment and storage medium, and belongs to the technical field of natural language processing. The method comprises the following steps: obtaining a current user instruction; classifying the current user instruction based on a text classification model to obtain an instruction type to which the current user instruction belongs; if the instruction type to which the current user instruction belongs is a device control type instruction or a system setting type instruction, extracting a first keyword from the current user instruction; searching for a second keyword matched with the first keyword from a mapping dictionary to obtain a search result; and if the search result shows that the mapping dictionary contains the second keyword matched with the first keyword, taking a standard instruction corresponding to the second keyword as a semantic understanding result of the current user instruction. The multi-scene semantic understanding method and device, electronic equipment and storage medium provided by the application can improve the semantic understanding accuracy of user instructions in multiple scenes.

This invention discloses a selection-based reading comprehension method, apparatus, storage medium, and electronic device based on the Dot Triple-Attention mechanism, belonging to the fields of natural language processing and artificial intelligence. The technical problem this invention aims to solve is how to more efficiently utilize text information to promote interaction between questions and options, and how to achieve direct interaction between the three sequences, thereby improving the prediction accuracy of the selection-based reading comprehension system. The technical solutions adopted are: ① A selection-based reading comprehension method based on the Dot Triple-Attention mechanism, comprising the following modules: a pre-trained embedding representation module, a feature filtering module, a Dot Triple-Attention interaction module, a feature aggregation module, and a label prediction module. ② A selection-based reading comprehension apparatus based on the Dot Triple-Attention mechanism, comprising: a pre-trained embedding representation module unit, a feature filtering module unit, a Dot Triple-Attention interaction module unit, a feature aggregation module unit, and a label prediction module unit.

This invention relates to a selection-based reading comprehension method based on multi-view graph encoding within a joint learning framework. First, this invention uses a multi-view graph encoding network to jointly encode documents, questions, and candidate answers from multiple different perspectives. It captures the relationships between sentences in the document and between document sentences and questions from three perspectives: statistical characteristics, relative distance, and deep semantics, fully mining potential evidence information to obtain document encodings for question-answer pair perception. Then, a binary classifier is used to determine whether each sentence in the document is an evidence sentence, thus implementing the function of the evidence extraction module. Finally, an answer prediction module is constructed, using the probability of document sentences obtained from the evidence extraction module as evidence to weight and selectively fuse the document encodings obtained from the multi-view graph encoding network. Both modules are trained simultaneously within the joint learning framework, thereby achieving the goal of answer prediction. This invention has achieved good results on selection-based reading comprehension tasks.

The invention provides a spoken language understanding method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting a voice feature map into a feature extraction module in a spoken language understanding model, and obtaining a shared representation matrix; inputting the shared representation matrix into a modal conversion module in a spoken language understanding model to obtain initial text representation; inputting the initial text representation into a text enhancement module in a spoken language understanding model to obtain an enhanced text representation; and inputting the enhanced text representation into a structured information generation module in the spoken language understanding model to obtain structured information corresponding to the speech to be recognized. Through the method, semantic logic coherence and text element relevance can be effectively enhanced, the semantic consistency learning ability of spoken language understanding is remarkably improved, the accuracy of structured information output and the model generalization ability are greatly improved, and the semantic understanding and intelligent response level of a machine to natural spoken languages is enhanced.

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 method, system, model, and medium for semantic understanding of sign language videos based on cross-modal attention, relating to the interdisciplinary field of computer vision and natural language processing. The method includes: preprocessing and sampling the input sign language video to obtain a standardized frame sequence; extracting visual features from the standardized frame sequence to obtain a visual feature sequence; inputting the visual feature sequence into a temporal modeling module to generate an enhanced spatiotemporal context representation sequence; projecting the spatiotemporal context representation sequence through a visual language projection layer to generate a visual key matrix and a visual value matrix; introducing a cross-modal fusion attention sublayer to generate a context vector based on the visual key matrix and visual value matrix; and generating a complete translated text based on the context vector using a pre-trained autoregressive language model, achieving high accuracy and high fluency in sign language translation.

Provided in the embodiments of the present application are a semantic understanding method and apparatus, and a medium and a device. The method comprises: calculating a first matching degree between an event to be processed and a preset event template; performing statement expansion on the event to be processed to obtain a similar event; calculating a second matching degree between the similar event and the preset event template; and obtaining a first result and a second result according to the first matching degree and the second matching degree, and using the preset event template for the greater one of the first result and the second result, so as to perform semantic understanding. By means of the present application, the recognition capability of a dialogue system can be improved.