1593 results about "Text corpus" patented technology

The invention provides a multi-source heterogeneous corpus fusion method and system based on government affair service data, and the method comprises the steps: obtaining an original corpus set of a plurality of data sources in government affair service, carrying out the cross-modal semantic alignment processing of each corpus unit in the original corpus set, generating a normalized data block corresponding to each corpus unit, and carrying out the fusion of the data blocks; carrying out multi-modal semantic coding on the standardized data blocks to obtain semantic feature vectors of all corpus units, carrying out topological structure coding on association attribute sets among the standardized data blocks to generate a global structure relation graph, and carrying out dynamic weight distribution on the semantic feature vectors based on node connection weights in the global structure relation graph to obtain semantic feature vectors of all corpus units; and generating a fusion weight matrix, performing cross-modal feature fusion on the semantic feature vector to obtain a target semantic embedding representation, and generating a standardized corpus associated with the government affair service. According to the method, the semantic aggregation problem of the non-uniformly distributed corpus units is solved, and the government affair data governance efficiency and the cross-department cooperation capability are greatly improved.

The invention discloses a knowledge graph construction method and system based on traditional Chinese medicine classics, and relates to the technical field of knowledge graph construction, and the method comprises the steps: collecting multi-source heterogeneous data of traditional Chinese medicine classics, carrying out the standardization processing, and generating a cross-time aligned standardized corpus through optical character recognition and multi-modal feature extraction; based on a language model and a causal reasoning engine, extracting an entity relationship and a dialectical rule from the standardized corpus, and generating a dynamic dialectical reasoning model supporting nonlinear reasoning; obtaining an output result of the dynamic dialectical reasoning model, and constructing a redundancy-removed and dynamically-calibrated traditional Chinese medicine knowledge base through knowledge fusion and conflict resolution of various traditional Chinese medicine classics; based on edge calculation and privacy protection technologies, real-time iteration and clinical curative effect feedback optimization of the knowledge graph are realized. According to the method, the problems of trans-era evolution, regional expression difference, multi-modal feature splitting and the like in the traditional Chinese medicine classics digitalization process are solved.

Embodiments of the present invention provide an innovative Retrieval-Augmented Generation (RAG) system tailored for financial analysis, significantly enhancing the precision and contextual relevance of Large Language Models (LLMs). A part of the system is a query augmentation component that leverages a knowledge graph to semantically enrich user queries, ensuring comprehensive retrieval of pertinent financial documents. A noise filtering mechanism refines the search results, while a relevance ranking component prioritizes documents based on context (e.g., user and task). The system employs prompt engineering to guide the LLM in generating responses that meet the specific requirements of financial analysis. Additionally, the LLM is fine-tuned using a corpus of financial questions and answers, reinforced by human-in-the-loop feedback, to adapt the model to the financial domain's unique linguistic and structural nuances. This advanced RAG system offers financial professionals timely, reliable, and actionable insights, providing a competitive edge in a rapidly evolving financial landscape.

The invention relates to the technical field of intelligent question answering methods, in particular to a case question answering method based on a large language model, a medium and equipment. The method specifically comprises the steps of obtaining graphic and text information and related questions of a case; and inputting the intelligent question and answer model to obtain an optimal answer. The intelligent question and answer model is obtained through the following steps: training a pre-training model through a constructed multi-modal corpus based on transfer learning to obtain a base model; based on LoRA and P-Tuning, performing fine tuning on the parameters of the base model to obtain a fine-tuned large language model; introducing a CLIP module to perform feature fusion on the graphic and text information to obtain a preliminary question and answer model; introducing a thinking chain to obtain a generation model capable of outputting a plurality of candidate answers with reasoning chains; and training an existing scoring model to obtain an evaluation model capable of outputting an optimal candidate answer. According to the method, the performance of the question answering technology in the aspect of generating accurate and reliable answers can be improved, and error information or illusion is reduced.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

The invention relates to the technical field of intelligent customer service, and discloses an intelligent customer service dialogue generation method and system based on user intention recognition, and the method comprises the steps: obtaining an input first user text from an intelligent customer service system, and carrying out the feature decomposition and attention fusion of the first user text, and obtaining a fusion intention feature matrix; performing intention migration analysis and dynamic Bayesian analysis on the dialogue sequence data in the historical dialogue corpus to obtain an intention state transition probability matrix; an improved Transform network model is input for joint training, and a dialogue generation model is obtained; a newly input second user text in the intelligent customer service system is decoded and generated based on the dialogue generation model, meanwhile, the intention state transition probability matrix serves as a constraint condition, a reply text conforming to the dialogue intention is obtained, the accuracy and robustness of intention recognition are improved, the reply smoothness is guaranteed, and meanwhile the user experience is improved. And the generated content is ensured to conform to an expected intention transfer rule.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

Apparatus and methods are disclosed for implementing a copilot as a network of microservices including specialized large language models (LLMs) or other trained machine learning (ML) tools. This architecture supports flexible, customizable, or dynamically determinable dataflow. Compared to much larger competing LLMs, comparable or superior performance is achieved for certain tasks, while significantly reducing computation time and hardware requirements, even to a single compute node with a single GPU. An expansion microservice converts client input tokens into associated tokens to diversify targeted tasks presented to a core microservice. An ML tool in the expansion microservice is pretrained in multiple stages for varying tasks using varying general and target-specific corpora. Synthesized training data derived from a knowledge graph can also be used. The expansion ML tool is subsequently fine-tuned in one or multiple stages. Variations and additional techniques are disclosed.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention provides a traditional Chinese medicine knowledge question-answering system based on a fine-tuning large model and dual retrieval enhancement. The traditional Chinese medicine knowledge question-answering system comprises a large model fine-tuning module, a dual retrieval enhancement module, a prompt template module and an answer generation module. The large model fine tuning module constructs a high-quality corpus by using traditional Chinese medicine ancient books, clinical cases and the like, performs incremental pre-training and supervised fine tuning on a ChatGLM3-6B pre-training model, and adopts technologies such as low-rank adaptation (LoRA) and direct preference optimization (DPO) to improve the adaptability of the model to traditional Chinese medicine professional knowledge. The dual retrieval module enables the model to map user questions to related contents in traditional Chinese medicine classical literatures, guidelines and modern literatures through text retrieval based on a LangChain framework on one hand, and provides structured background knowledge through map retrieval on the other hand. The system effectively makes up for the knowledge blind area of a large-scale general model in the field of traditional Chinese medicine, realizes the improvement of the accuracy, continuity and speciality of question and answer results, and has wide application prospects and relatively high innovativeness.

The invention discloses a milk industry knowledge question-answering method and device based on a large model and RAG and a medium, and the method comprises the steps: collecting multi-source heterogeneous data of the milk industry, carrying out the term protection word segmentation processing of the multi-source heterogeneous data, and generating a labeled field corpus; based on a pre-training language model base, through a domain corpus injection and adversarial learning mechanism in a domain corpus, generating an enhanced language model adapted to the dairy industry terminology; receiving a milk industry problem of a user, executing semantic vector retrieval and term extension retrieval in parallel, and screening a multi-modal retrieval result through a dynamic sorting algorithm; and splicing the multi-modal retrieval result and the milk industry question into an enhanced prompt, inputting the enhanced prompt into an enhanced language model, generating a final answer corresponding to the milk industry question, and associating the final answer with a knowledge source.

Methods, systems, and computer program products for collaboration systems. A method for identifying selected portions of a set of content objects for use in generating a large language model (LLM) prompt comprises: identifying a content management system (CMS) wherein collaboration activities occur over time and over content objects maintained in the CMS, and wherein the CMS maintains a historical record of occurrences of the collaborator activities over the content objects. Upon receiving a natural language query from a CMS collaborator, reducing a larger corpus of content objects to a smaller corpus of context passages that are used in an LLM prompt. The smaller corpus of passages is formed using a two-phase reduction scheme whereby firstly, selected constituents from the larger corpus of content objects are identified based on CMS metadata; and then, rather than considering the larger corpus, instead considering only the selected constituents when generating the LLM prompt.

A data processing system implements receiving, via a user interface of a client device, a query to a large language model (LLM) working in conjunction with an embedding model comprising a corpus of artifacts. The system further implements converting the query into a query embedding, transforming the query embedding into a transformed query embedding using a first transformation function that adds a first residual term to the query embedding, the first residual term being specific for a task associated with the query, measuring a first similarity between the transformed query embedding and corpus element embeddings in an embedding space of the embedding model, determining first top-rated corpus element(s) associated with the corpus element embeddings based on the first similarity, incorporating the first top-rated corpus element(s) into the query as a prompt to the LLM to generate a response to the query, and providing, via the user interface, the response.

Provided in the present invention are a method for training a natural language processing model, and a method for generating subsequent text of a dialogue. The method for training a natural language processing model comprises: acquiring multiple types of heterogeneous sample data and a pre-constructed natural language processing model, wherein the heterogeneous sample data includes structured data, unstructured data, a knowledge graph and expert experience data; performing data encoding fusion on the structured data, the unstructured data and the knowledge graph, so as to obtain encoding fused data; and on the basis of the encoding fused data and the expert experience data, training the pre-constructed natural language processing model, so as to obtain a trained natural language processing model. The present invention enables a knowledge graph to be placed in a model, not as an independent retrieval corpus, but as a method for knowledge enhancement, thereby improving the efficiency of a natural language processing model.

The invention discloses a knowledge graph-based nursing corpus automatic creation method, and relates to the field of artificial intelligence technology and medical information processing, and the method comprises a multi-dimensional data acquisition stage, a knowledge graph construction stage, a corpus automatic generation and updating stage, and a corpus quality control and evaluation stage. According to the automatic corpus generation method driven by the knowledge graph, dynamic generation and real-time updating of corpora are achieved by constructing an ontology structure, a corpus generation template and an updating algorithm in the nursing field; through a deep learning model and a transfer learning technology, new nursing knowledge and a diversified information structure are continuously introduced into the corpus, and the diversity of corpus content is increased.

The present disclosure leverages self-supervised learning to generate positive and negative question-context pairs, enabling the model to learn robust representations. This process involves data augmentation techniques to create variations of the original questions and contexts while preserving semantic relevance. A large corpus of unlabelled text data containing questions and their corresponding contexts, ensuring diversity and representativeness across various topics are used to train a self-supervised Large Language retrieval model.

The invention provides a multimodal reasoning method based on a Monte Carlo tree and dynamic retrieval. The method is applied to an artificial intelligence assistant-assisted teacher paper judgment scene. Specifically, the framework comprises a unified retrieval module, wherein the retrieval module comprises a mixed modal retrieval corpus and a multi-modal retrieval module; the multi-modal retrieval module comprises two retrieval modules, namely a text retrieval module and a cross-modal retrieval module, key problem solving insights are selected by adopting knowledge concept filtering, and progressive multi-modal reasoning annotations of multi-modal reasoning are automatically obtained by utilizing Monte Carlo tree search and an active retrieval mechanism for data annotation; and gradually aligning a process reward model through course process reward modeling in two stages by utilizing the annotation data so as to realize fine-grained verification of multi-modal large model reasoning. An MCTS algorithm and an active retrieval strategy are combined to improve multi-modal reasoning. The framework can automatically obtain high-quality step-by-step reasoning annotations, so that a process reward model is aligned step by step, and finally reliable automatic multi-modal reasoning verification is realized.

The invention relates to the technical field of port vertical large model systems, in particular to a port vertical large model system based on a general large language model and a construction method. Comprising the following modules: a data acquisition module, a pre-training corpus construction module, an incremental pre-training module, a fine adjustment module, a port adaptation and optimization module, a knowledge base management module, a reasoning and man-machine interaction module and a system monitoring and optimization module. The port vertical large model has the beneficial effects that the port vertical large model can dynamically adapt to emergencies in port operation through incremental pre-training, a fine tuning technology and a real-time knowledge base updating mechanism, and multi-source data is integrated and analyzed to form a whole-process business simulation and optimization scheme. The modularized knowledge management mechanism enables the port management system to have a real-time decision support function, and improves the overall efficiency and safety of the port in the aspects of equipment maintenance, cargo scheduling, environment monitoring and the like.

The invention belongs to the technical field of knowledge graph construction, and discloses an urban rainstorm flood disaster chain affair graph construction method and system, and the method comprises the steps: firstly obtaining a research document, a first historical event and a secondary event, constructing a disaster chain directed network graph, and constructing an ontology model according to the disaster chain directed network graph; acquiring a second historical event and a secondary event, preprocessing, and inputting into the ontology model to obtain an event corpus; extracting event trigger words from a corpus to build a database by using a multi-modal dynamic fusion model MDF-Net; performing event extraction in a corpus according to the trigger word to obtain an event type and an argument; extracting a time sequence, a spatial relationship and a semantic relationship of a trigger word by using an improved ELCTRA-BiLSTM model; and finally, constructing a disaster chain affair map by taking the event type, the argument, the time sequence and the spatial relationship as nodes and taking the semantic relationship as an edge. According to the method, multi-source data and complex disaster information can be effectively integrated, and the integrity and accuracy of the urban rainstorm flood disaster chain affair map are improved.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intention analysis and strategy generation method, device, equipment and medium, and the method comprises the steps: obtaining interaction data, recognizing an interaction type according to metadata features, selecting a corresponding analysis model to analyze and process data, and generating a text corpus; current user information is extracted from the text corpus, user intention score analysis is executed based on an intention analysis strategy corresponding to the interaction type, and a current user intention score is generated; and generating a demand list according to the user information, the intention score and the interaction type, and generating and outputting an interaction strategy based on the user information and the demand list. Through interaction data processing and automatic intention scoring analysis, the demand list and the interaction strategy of the customer can be quickly and accurately generated, the customer information processing efficiency and accuracy are improved, the workload of manual input and analysis is reduced, and the customer service quality and the response speed are improved.

Existing question answering approaches have the disadvantages that they possess limited contextual understanding due to which the retrieval process they use is inefficient in nature. Embodiments disclosed herein provide a method and system for domain-driven knowledge augmented question answering. The system receives a raw corpus data as input, wherein the raw corpus data is a domain specific data. Further, an indexed corpus is generated from the raw corpus data, during which a document chunking approach is used. The indexed corpus is then used for processing received queries received, in order to generate response to the received user queries.

The invention belongs to the field of language processing, particularly relates to a multi-causal relationship extraction method and system for intelligent evaluation, and aims to solve the problems of performance bottleneck, remarkable wooden barrel effect and insufficient adaptability and integration caused by dependence on multiple independent models. The method comprises the steps that a causal analysis prompt template used for generating step-by-step reasoning is constructed to serve as a thinking chain prompt word, a fine adjustment corpus is generated in combination with an original corpus and input into a large language model for training, a low-rank adaptation technology is adopted in the training process to conduct fine adjustment training on the large language model, and a causal relationship extraction model is obtained; inputting corpora of a to-be-extracted causal relationship into the causal relationship extraction model, and outputting a causal relationship extraction result; and generating a comprehensive evaluation result and a decision suggestion. According to the method, the efficiency and the quality of the multi-causal relationship extraction task are improved, the stability and the adaptability of the system are enhanced, meanwhile, the labor cost is reduced, and scientific and reliable evaluation results and decision suggestions are provided.

The disclosure relates to systems and methods of automatically generating unique content including natural language text based on a corpus of previously generated response documents and discrete requirements defined in a requirements specification. The system may use generative stitching that includes multi-layer processes that execute to influence the generation of unique content including natural language text through an artificial intelligence (AI) language transformer model trained to output the content based on previously written material that is semantically relevant to the discrete requirements and is weighted against labeled attributes. The labeled attributes may determine the influence asserted against the language transformer, thereby generating unique on-target content that may be combined to create a computer-generated response document.

The invention discloses a method for constructing a nursing field text annotation corpus based on deep learning, and relates to the field of artificial intelligence technology and medical information processing. Comprising a data collection and preprocessing step, a BERT-based nursing field entity recognition model construction and training step, an automatic labeling and post-processing step, a corpus construction and management step and an application and intelligent support step. According to the method, the problems of efficiency and accuracy of text labeling in the nursing field are solved, and the continuously developing and changing text data processing requirements in the nursing field can be better met. The entity recognition model architecture specially aiming at the text characteristics in the nursing field is constructed based on deep learning, and challenges can be effectively handled when complex and diversified nursing text data is processed. And meanwhile, by utilizing the automatic labeling capability of deep learning, the labeling efficiency is improved, the manpower and time cost is reduced, and a new direction, a new mode and new experience are provided for nursing informatization development.

The invention relates to a fuzzy test Kubernete-based three-party component vulnerability mining method, which comprises the following steps of: firstly, automatically identifying a user controllable function by combining a large language model based on an official document, a configuration list and source code information of a three-party component, and constructing a keyword dictionary to assist in large-scale identification of a potential target function; then analyzing context information of the target function and an interaction relation of the target function in a Kubernetes cluster, constructing a front dependency library required by fuzzy testing, and automatically generating a fuzzy testing driving program; integrating a fuzzy test driving program with a three-party component source code, performing code instrumentation, pre-compiling and corpus optimization, generating test input according to a set variation strategy, dynamically monitoring a coverage rate and an abnormal behavior, and continuously optimizing a test corpus; and finally, based on the crash behavior of the fuzzy test record, analyzing a trigger path of the vulnerability, performing vulnerability verification in a local Kubernetes cluster environment, and finally generating a vulnerability analysis report.

The invention discloses a drilling and blasting method tunnel construction safety risk evolution analysis method based on a knowledge graph, and the method comprises the steps: obtaining and preprocessing the text data of a drilling and blasting method tunnel construction accident case, and obtaining a safety risk corpus; a mixed deep learning model is constructed, the safety risk corpus is trained through the mixed deep learning model, and the risk factors and the incidence relation of drilling and blasting method tunnel safety are obtained; constructing a drilling and blasting method tunnel construction safety risk coupling evolution analysis knowledge graph based on the risk factors and the incidence relation, and obtaining a key accident chain and a key edge in combination with a knowledge query and path reasoning algorithm; and quantitatively evaluating independent values and coupling effect intensity of the risk factors by using an interaction matrix algorithm, calculating comprehensive importance scores of the risk factors, and determining key risk points according to the comprehensive importance scores of the risk factors. The problems that in a traditional method, attention to the risk factor coupling effect is insufficient, and accident case data are not fully utilized are effectively solved.

A method and system for creating an enhanced domain-specific language learning model is disclosed. In some embodiments, the method includes training a domain language model using domain-specific data. The method includes receiving input corpus for one or more downstream tasks. The method then includes using the domain language model using the input corpus to generate a first set of embeddings, and using a pre-trained large language model (LLM) using the input corpus to generate a second set of embedding. The method further includes combining the first and second sets of embeddings to form a combined set of embeddings and perform the one or more downstream tasks using the combined set of embeddings.

The invention relates to an ancient character image recognition and semantic analysis method, which comprises the following steps of: firstly, acquiring an original image containing irregular deformation and material diversity characteristics from the surface of a cultural relic, and eliminating noise and illumination interference through adaptive filtering; calculating a main inclination angle based on stroke feature distribution, and executing inclination angle correction to obtain a processed image; thirdly, separating independent characters by adopting a region growing algorithm based on stroke features, and extracting feature vectors by utilizing a deep convolutional network for identification; and finally, matching context information in combination with an ancient text corpus, and optimizing sequence labeling by adopting a conditional random field algorithm to obtain semantic output. And if the result is not ideal, the inclination angle correction parameter is adjusted through backtracking and iterative optimization is carried out, so that the overall identification accuracy is improved. According to the method, the problems of irregular deformation and material diversity in the ancient character image can be effectively solved, and the recognition precision and the semantic analysis reliability are improved.

The invention discloses a question and answer generation method based on a knowledge graph, and relates to the technical field of artificial intelligence and natural language process.The method comprises the steps that a bidirectional semantic encoder is adopted to process input text corpus, entity words and relational words are extracted, a first semantic vector is formed, and the first semantic vector is converted into a basic knowledge structure through a triple decomposer; aligning the basic knowledge structure with deep semantic information in a preset knowledge graph by using a hierarchical knowledge fusion network to generate a multi-dimensional knowledge feature matrix, calculating association strength among knowledge nodes by using a dynamic weight distributor, and constructing a hierarchical question and answer path according to a sequence of the association strength from high to low; and inputting the hierarchical question and answer path into a constructed attention model, and generating a question and answer pair data set according to a question and answer template library. According to the method, the knowledge extraction accuracy and the knowledge fusion effect are improved, the question and answer path construction process is optimized, and a question and answer pair data set with a complete structure is formed.