1358 results about "Named-entity recognition" patented technology

Device, system, and method for automatically detecting and classifying personally identifiable information (PII) in documents and files. A method includes performing a deterministic rule-based search, in a plurality of stored documents, for PII data-items. If the deterministic rule-based search indicates that a particular document is more likely than not to contain a PII data-items then the method includes: extracting a textual snippet from the particular document, wherein the textual snippets surrounds the PII data-item; adding the textual snippet and the particular document to one or more training datasets utilized for training a Large Language Model (LLM) configured to find PII data-items in documents for Named Entity Recognition (NER) in those documents.

The invention relates to a multi-modal named entity recognition method based on cross-modal guide interactive fusion, which comprises the following steps: constructing a data set, designing a cross-modal contrast aggregation mechanism, respectively extracting image features and text features, and constructing a contrast learning mechanism to screen out image features with high association degree with text semantics for dynamic aggregation; a DINO model is introduced to extract image features, a dynamic similarity matching method is constructed, a dynamic similarity matching weight is generated based on text features and an image feature correlation matrix, a dynamic gating mechanism is utilized to adaptively select image features related to text feature context, and a cross-modal fusion and guide interaction strategy is constructed. Outputting an enhanced semantic representation vector, and mapping the semantic representation vector after multi-modal fusion into a final entity tag sequence by adopting a conditional random field decoder to complete entity recognition; the method has the advantage of remarkably improving the robustness and accuracy of multi-modal entity recognition.

A schema and cell value aware Named Entity Recognition (NER) model is used to perform natural language queries. Natural language queries may be received via an interface of a natural language query processing system. A fuzzy search may be performed that allows non-exact matches for column names or cell values of data sets potentially used to answer the natural language query. An NER model that adds a type embedding for an exact match of a column name or cell found in the fuzzy search that corresponds to a span of one or more words may be applied as part of generating the entity prediction for the natural language query. One or more queries to at least one of the data sets may be performed to return a result to the natural language query using the entity prediction generated by the NER machine learning model.

The invention discloses a multi-modal entity and relation extraction method and system based on cross-modal alignment and fusion, and the method comprises the steps: carrying out the processing and coding of an input text and an image, and obtaining a plurality of types of image and text features; performing feature alignment on the fine-grained and coarse-grained text features and the pixel-level image representation by taking the semantic representation of the image as an anchor point, and mapping the image and the text features to the same semantic space; performing multi-granularity feature fusion through text-guided dynamic gating aggregation, visual prefix cross-modal fusion and cross-modal image-text matching, modeling association between noun phrases and image objects in a text while increasing feature complementarity, and obtaining multi-granularity multi-modal feature representation; fusing multi-granularity multi-modal features through entity guidance attention gating, and gathering visual information related to a text entity to obtain final multi-modal fusion representation; according to the multi-modal fusion representation, task prediction of multi-modal named entity recognition and multi-modal relation extraction is carried out.

The invention relates to the technical field of model review, in particular to a BIM model review method based on text classification and named entity recognition, and provides the following scheme: building a building specification knowledge graph, obtaining BIM review data through a review data extraction strategy, obtaining a matching rule set by utilizing knowledge graph matching, and adjusting the priority for review. Outputting the report; the knowledge graph construction adopts an ontology model and a knowledge extraction network, component attributes, spatial relationships and time dimensions are considered in the examination data extraction, the matching process is combined with multi-dimensional semantic matching and reasoning, and the examination accuracy and efficiency are ensured.

The invention discloses a power transformer fault auxiliary decision-making method and system based on a knowledge graph and a large language model, and the method comprises the steps: obtaining power transformer fault text data, carrying out the preprocessing, obtaining fault-related text and table data, preliminarily defining an ontology, selecting a part of text data for marking, and carrying out the recognition of the ontology; a plurality of named entity recognition and relation extraction models are trained, an optimal model is determined, then triple extraction is carried out on unlabeled text data, and a knowledge graph is constructed; predicting a potential entity relationship in the knowledge graph based on a link prediction model, and complementing the knowledge graph under the large language model and human assistance; constructing a semantic search model training data set based on the large language model and training a semantic search model; based on the complemented knowledge graph, the trained semantic search model is utilized to retrieve knowledge graph sub-graphs according to the problem, and auxiliary decision making is completed. According to the method, the problem of low potential knowledge utilization level in the existing power transformer fault auxiliary decision-making based on the knowledge graph is solved.

The invention discloses a large model scheduling multi-agent power grid fault coping strategy knowledge graph extension method and system, and the method comprises the steps: carrying out the modeling of task distribution as a mixed integer programming problem, and achieving the solving through a relaxation-correction algorithm; extracting and normalizing a core task of knowledge, and outputting structured data; summarizing the obtained text segments, and prompting a large language model to retain key entities, relationships and domain-specific terms; using an LLM-based named entity recognition technology, combining with prompt and dictionary / ontology filtering in the power dispatching field, recognizing related entities in a text, and normalizing the related entities into a standard form in a knowledge graph; detecting logic contradictions between the newly extracted triples and existing relationships in the knowledge graph, and classifying and solving the contradictions by utilizing debate prompts based on LLM (Logistics Library Model); summarizing the plurality of verification signals, and calculating the global confidence, definition and correlation score of each triple;

Aspects of the present disclosure provide techniques for enhanced electronic data retrieval. Embodiments include receiving a natural language query and identifying one or more electronic data sources indicated in the natural language query using a named entity recognition (NER) machine learning model trained through a supervised learning process based on training natural language strings associated with labels indicating entity names. Embodiments include determining one or more additional electronic data sources related to the one or more electronic data sources using a knowledge graph that maps relationships among electronic data sources. Embodiments include retrieving data related to the natural language query by transmitting requests to the one or more electronic data sources and the one or more additional electronic data sources and providing a response to the natural language query based on the data related to the natural language query.

The invention provides a structured processing method and device for a contract text, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of the contract text, generating a standardized word sequence, and enabling the standardized word sequence to comprise a plurality of word sequences and part-of-speech tagging information of each word sequence; the standardized word sequence is input into a named entity recognition model to recognize key entities in the contract text, key terms in the contract text are extracted based on a machine learning algorithm, the key entities include both parties of the contract, the amount of the contract and the name of a product, and the key terms include payment terms, delivery terms, default responsibility terms and force majeure terms; through dependency syntactic analysis and a semantic role labeling model, analyzing to obtain a semantic relationship between the key entities and the key terms; and storing the extracted key entities, key terms and semantic relationships in a target database in a structured form, and establishing a target index to support quick query. According to the invention, the error rate of manual processing is reduced.

The invention relates to a recommendation method based on a knowledge graph, and the method comprises the steps: carrying out the semantic analysis of knowledge data of a target domain through a bidirectional LSTM auto-encoder, and generating a semantic vector; in combination with a semantic vector and named entity recognition technology and a semantic role labeling technology, entities and relationships thereof are recognized, a triple is constructed, and a structured knowledge graph is generated. Node feature extraction and embedded representation are performed in a knowledge graph, an initial graph neural network model is constructed in combination with graph structure information, a target graph neural network model is generated through training and optimization, nodes are scored and sorted, and a target object is recommended. According to the invention, the accuracy and intelligent level of recommendation can be improved, the personalization and matching degree of recommendation results are enhanced, and the problem of inaccurate recommendation results in the prior art is significantly improved.

The invention discloses a relation extraction method and system based on a graph neural network, and belongs to the technical field of natural language processing. A target text is obtained, word segmentation, part-of-speech tagging and named entity recognition are carried out, and an entity set is extracted; constructing a text graph structure containing multiple edge types based on the entity set; performing feature coding on nodes in the graph to generate an initial feature vector fusing semantic, part-of-speech and position information; inputting the graph into the graph neural network model, and obtaining high-order node representation through multi-layer message passing and aggregation; modeling the entity pair in combination with the structure path and the context information, and inputting a multi-channel classification network to predict the relationship type of the multi-channel classification network; and finally, outputting an entity relationship triple according to a prediction result. The method has stronger semantic modeling ability and structure expression ability in a relation extraction task, and is suitable for scenes such as knowledge graph construction and information extraction systems.

The invention discloses an intelligent traditional Chinese medicine auxiliary diagnosis system based on knowledge graph retrieval enhanced generation, the system comprises a knowledge base and an online reasoning module, the knowledge base comprises a vector knowledge base and a graph knowledge base, and the graph knowledge base is obtained by performing knowledge extraction and entity recognition on a traditional Chinese medicine original text data set through a large language model LLM; vectorizing entities in the map knowledge base to obtain a vector knowledge base; the online reasoning module adopts a large language model LLM to perform named entity recognition on a user question to obtain entities related to traditional Chinese medicine, a triple containing the most related entities is inquired in a graph knowledge base, the inquired triple serves as known knowledge, and the triple and the user question are spliced together to serve as cue words to be input to the large language model LLM. According to the system, professional traditional Chinese medicine knowledge is used as a knowledge base, a large model is used as a tool for knowledge arrangement and summarization, and user requirements are accurately recognized in a man-machine interaction mode.

The invention belongs to the technical field of natural language processing and multi-modal information extraction, and particularly relates to a multi-modal named entity recognition method based on semantic alignment and cross-modal graph fusion, which comprises the following steps: S1, acquiring a data sample containing a text sequence and image content; s2, encoding the text and the image into vectors respectively; s3, similarity is calculated through a trainable bilinear function, and optimization is carried out through loss comparison; s4, cross-modal attention is used to enhance association information between modals; s5, determining the proportion of reserved image information through a modal matching module; s6, introducing a gating mechanism to dynamically fuse visual and text features; s7, realizing local and global information complementation by a cross-modal graph fusion model; and S8, inputting the fused representation into the CRF layer to predict the entity type. According to the method, fine semantic alignment can be realized in a weak image-text correlation context, and balance between local entity recognition and global semantic understanding can be achieved.

A system to analyse impact of data breaches on sensitive data is disclosed. The system includes a hardware processor and memory with program instructions for executing various modules. The data collection module retrieves and enriches impacted data from multiple repositories. The data identification module uses data loss prevention (DLP) and named entity recognition (NER) techniques, enhanced by large language models (LLMs), to accurately identify personal information. The identity deduplication module consolidates individual references using deterministic and probabilistic techniques, while the residency inference module applies machine learning and heuristic methods to determine residency based on various data sources. The analysis module assesses impacted data to identify relevant laws and estimate fines. The automation module streamlines response actions, including generating notifications and ensuring compliance. This system enhances breach response efficiency through integrated, automated analysis and actions.

In one embodiment, a method of training one or more artificial intelligence (AI) models for language-based communication prompts with a service provider is disclosed. The training method includes generating industry specific labels used to fine tune a large language model; providing an industry specific database associated with the industry specific labels to fine tune the large language model; reading the industry specific database into the large language model; adjusting the parameters of the large language model to recognize industry specific terms associated with servicing equipment within the industry; adjusting the parameters of the large language model to discover the intent associated with the industry specific terms; and adjusting the parameters of the large language model for industry specific tasks including questions and answer tasks, named entity recognition, classification tasks, and machine translations tasks.

The invention provides a translation semantic correction method and system based on a large language model. Belongs to the technical field of natural language processing. The method comprises the following steps: collecting data; performing named entity recognition by using a large language model, extracting specific entities from a source language text, and analyzing a relationship between the entities through LLM to generate an entity pair relationship; identifying and extracting attributes of the entities, and associating the attributes with the corresponding entities; integrating the extracted entities, relationships and attributes according to a graph structure, and constructing a preliminary knowledge graph; inputting the source language text into the model for translation; terms in the preliminary translation result are compared with the knowledge graph, and potential semantic deviation is recognized. And performing quality evaluation on the translation result. By integrating the identified and extracted relationship and attribute between the entities into the knowledge graph, the semantic relationship between the entities can be understood more deeply, so that the subsequent translation is more in line with the actual context.

The invention discloses a small sample learning method and system based on data enhancement. Collecting electric power operation and maintenance text data from a plurality of data sources, preprocessing and labeling the data, and constructing an electric power field corpus; performing fine tuning on a predefined pre-training language model by using the power field corpus, training power field word vectors and entity vectors from the power field corpus, and fusing the power field word vectors and entity vectors into the pre-training language model; performing named entity recognition and multi-task learning of power operation and maintenance knowledge based on a pre-training language model, constructing a multi-task adversarial learning model in combination with an adversarial learning algorithm, training the multi-task adversarial learning model by adopting multiple data enhancement strategies, and performing data prediction through integrated learning and self-training by utilizing the multi-task adversarial learning model; and evaluating a model prediction result, and dynamically adjusting the weight of each data enhancement strategy based on an evaluation result. The small sample learning performance is improved.

The invention discloses an AI-based large-model-driven contract review and regulation interpretation method and system, and the method comprises the steps: S1, collecting a regulation and institute document, and carrying out the text extraction and semantic disassembly, and obtaining term information; s2, based on a LawCheckLM + BERT-CRF named entity recognition model, performing entity recognition and classification labeling on clause information, and storing recognized key information fields into a knowledge base; s3, encoding each piece of clause information into a semantic vector through an embedded model, and storing the semantic vector into a knowledge base; and S4, performing text extraction and semantic disassembly on the uploaded contract document to obtain clause information, repeating the steps S2-S3, retrieving similar semantic vectors of laws and regulations or institutional documents similar to the contract from the knowledge base as references, if the similar semantic vectors are not retrieved, taking Top-N laws and regulations or institutional documents with similar semantics as outputs, and marking the Top-N laws and regulations or institutional documents as required to be examined. The problems that an existing management system is difficult to adapt to regulation changes, complex contract review and cross-scene deployment are achieved, and the overall iteration period is long are solved.

The invention discloses a geological domain named entity accurate recognition and classification method based on thinking chain enhancement and hybrid expert architecture, which is characterized by comprising the following steps: firstly, extracting geological document text data through an OCR (Optical Character Recognition) technology, and extracting structured entity data by utilizing a locally deployed large language model; then calling a local large model to generate a diversified sentence pattern template according to language styles in the geological field, filling the template with the extracted professional entities, and constructing an instruction fine tuning data set; further constructing a thinking chain (CoT) enhanced data set on the basis, and explicitly simulating an expert reasoning process; efficient fine tuning is carried out on the large model by innovatively combining a low-rank adaptation (DoRA) technology and a hybrid expert (MoE) architecture, the DoRA technology carries out dimension reduction decomposition and orthogonal transformation on weight matrixes of a decoder layer and a multi-layer perceptron, and the MoE architecture constructs a plurality of special sub-networks to enhance the multi-task processing capability; and finally, performing entity extraction on the geological document by using the fine-tuned model, outputting an identification result containing a reasoning process, and filtering and perfecting the result through a rule matching mechanism. According to the method, the problems of fuzzy boundary, indefinite semantics, difficulty in classification and the like of the named entities in the geological field are effectively solved, the recognition and classification accuracy of the named entities in the geological field is remarkably improved, and key technical support is provided for downstream applications such as geological resource exploration and mineral evaluation.

The invention discloses a named entity recognition method in combination with a large model and dynamic examples. The method comprises the following steps: S1, constructing a dynamic example library; s2, constructing a basic cue word: defining a business entity annotation background, a rule and an output specification, and constructing a structured cue word in an XML (Extensible Markup Language) style; s3, recalling a dynamic sample; s4, training corpus construction, namely entity recognition corpus construction and recognition reasoning corpus construction; s5, selecting a large model; s6, identifying the large model, integrating the instance d, recalling the sample set R and the basic cue word, inputting the integrated instance d, recalling the sample set R and the basic cue word into the trained model, and outputting a final entity identification result; and S7, API service packaging: recalling the dynamic sample, and packaging the prompt word and the selected large model into AP I service for convenient calling. According to the method, the problem of bad case identification can be rapidly repaired, powerful support is provided for rapid landing of new field and new demand expansion, and the manpower and time cost of data annotation is effectively reduced.

The invention discloses a production area cadmium and arsenic pollution collaborative prevention and control knowledge integration management method and system, and relates to the technical field of data integration management, in the method, a multi-thread parallel acquisition technology is used for acquiring massive network literatures related to cadmium and arsenic pollution, and core text data in the network literatures are extracted; the method comprises the following steps of: preprocessing the network literature, identifying and screening named entities to obtain a key sentence set, determining the category of the corresponding network literature based on the key sentence set by adopting a random forest algorithm, and clustering all the network literatures to obtain a network literature clustering result; and finally, constructing a cadmium and arsenic pollution knowledge graph and visually displaying the cadmium and arsenic pollution knowledge graph. According to the method, cadmium and arsenic pollution related data can be efficiently and automatically collected and subjected to standardization processing, the consistency and comparability of the data are ensured, meanwhile, a knowledge graph mode is adopted, multi-angle and multi-dimensional data analysis and display functions can be provided, and the requirements of users in different fields and different levels are met.

The invention provides a mediation document generation method and system, relates to the technical field of data processing, and comprises the step of forming structured case data through text preprocessing, named entity recognition and legal information association based on case text data and legal corpus information. Then, generating a preliminary mediation document with placeholders by utilizing case classification, semantic matching and a sequence-to-sequence model; after standard auditing and logic auditing are carried out on the documents, optimization is carried out based on reinforcement learning, language style migration and a text generative adversarial network, and the structural rationality, law term normativity and semantic integrity are improved. And finally, a mediator preference template is constructed through Few-shot Learning and meta learning, the format and language style of the document are adjusted in a personalized manner, and a mediation document conforming to the habits of a mediator is generated. According to the method, the intelligence and the accuracy of mediation document generation are improved, the law compliance and the logic preciseness are ensured, and the mediation working efficiency is improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention discloses a natural language question and answer method, system and device, a medium and a product, and relates to the technical field of semantic recognition. The method comprises the steps that a user question is recognized based on a named entity recognition model, and a key entity is determined; the key entity comprises a place name, a distance and an orientation in the question; performing text classification on the user question based on a user intention recognition model, and determining the intention of the user; determining a structured query statement based on a preset language model according to the key entity and the intention; obtaining multi-dimensional geographic space data in the Internet map; converting the multi-dimensional geographic space data into a triple, and constructing a structured knowledge graph; and according to the structured query statement and the structured knowledge graph, determining an answer to the user question. According to the method, the intention of the question of the user is accurately and intelligently understood, and the accurate answer is returned.

The invention relates to a security management knowledge graph construction and dynamic updating method based on association modeling, and the method comprises the steps: collecting multi-source heterogeneous data in the field of security management, carrying out the recognition of the data type, carrying out the data conversion, setting an active learning mechanism in a named entity recognition model, carrying out the training, and recognizing an entity from the data. The entity is extracted; constructing an attention-enhanced graph neural network to analyze image data, calculating an attention weight for each node and edge in a message transmission process, and updating node information to realize relation extraction; based on the entity and relational data, constructing a preliminary security management knowledge graph; reasoning and supplementing missing information and relations based on an external standard knowledge base; the data change increment is detected in real time to update the security management knowledge graph, nodes and relationships are newly added or updated in the security management knowledge graph, the security management knowledge graph is efficiently constructed and dynamically updated, and support is provided for cross-modal intelligent question answering and real-time security evaluation.

The invention relates to the technical field of agricultural information, in particular to a tobacco agriculture standard named entity recognition method and system based on a hybrid neural network, and the method comprises the steps: carrying out the embedded representation of an input tobacco agriculture standard text through a BERT pre-training language model, and generating a word vector sequence containing global semantic information; performing local feature extraction on the word vector sequence by using an iterative expansion convolutional neural network to obtain a local feature vector; splicing the global semantic information and the local feature vectors, and inputting the spliced global semantic information and local feature vectors into a bidirectional long-short-term memory network for context feature extraction to generate context enhancement features; weight optimization is carried out on the context enhancement features through a multi-head attention mechanism, and key semantic features are highlighted; and performing label prediction on the optimized feature sequence by adopting a conditional random field decoder, and outputting a standard article element entity identification result. According to the method, high-precision and high-robustness named entity recognition is realized, and the method is particularly suitable for complex semantic and low-resource field scenes.

Knowledge graph guide and entity controlled techniques for generating synthetic doctor-patient conversations. In one particular aspect, a method is provided that includes obtaining an original dataset containing textual dialogue associated with a plurality of individual doctor-patient conversations for training a machine learning model, constructing input data by using named entity recognition to capture and categorize named medical entities present in the dialogue, generating prepared input data by arranging the input data in an annotated turn-by-turn conversation format using an input data preparation algorithm having various control parameters, training the machine learning model using the prepared input data, utilizing a knowledge graph to identify a plurality of symptoms mapped to a randomly selected disease, and causing the trained machine learning model to generate a synthetic doctor-patient conversation by inputting the plurality of symptoms to the machine learning model as a first control parameter of a conversation generation control algorithm.

The invention aims to provide a named entity recognition method and device based on a large language model, and the method comprises the steps: carrying out the data cleaning of a to-be-trained text, carrying out the data enhancement of the cleaned data, and obtaining target data; the lora structure is fused into a large language model LLaMA model, and a new large language model is obtained; training the new large language model by using target data to obtain a target model; and extracting a target entity from the text by using the target model to complete identification of the named entity. Therefore, the problem of insufficient effect caused by less parameter quantity and knowledge in the traditional method is solved, the training data is increased, the effect of the model is improved, the training speed of the model is improved by adopting lora, and the problem of insufficient training resources is solved.

The invention provides an electronic medical record management method and system based on AI calculation. The method comprises the following steps: designing a general template of an electronic medical record; preprocessing the patient data; dividing the preprocessed patient data into patient numerical data and patient text data; marking key content items in all the universal content items in the universal template; performing abnormal data detection on the patient numerical data and the patient text data based on the target universal template; extracting a patient data entity and a content item entity from the target electronic medical record by using a named entity recognition model; constructing a medical record content graph network of the target electronic medical record in combination with the patient data entity and the content item entity; and performing abnormal content detection on the target electronic medical record based on the medical record content graph network, and if the target electronic medical record passes the abnormal content detection, storing the target electronic medical record. The method has the effect of intelligently generating the high-quality electronic medical record.

The embodiment of the invention discloses a document knowledge element extraction method and device based on a large model and a medium, relates to the technical field of knowledge maps, and is used for solving the problem that an existing mode is poor in accuracy and low in interpretability. The method comprises the steps of obtaining original documents of a specified field based on a multi-source interface, and performing part-of-speech tagging and named entity recognition processing according to document contents and document fields of the original documents to obtain standardized documents; corresponding domain terms are collected, a corresponding domain knowledge graph is constructed, and entities and relationships of the domain knowledge graph are mapped to a low-dimensional vector space for data amplification; inputting a preset large model according to the amplified domain knowledge graph, and performing domain fine tuning on the preset large model based on a preset strategy to obtain a domain adaptive model; and inputting a to-be-extracted document in a specified field into the field adaptation model to extract knowledge elements of the to-be-extracted document, screening important knowledge elements based on the attention scores corresponding to the knowledge elements, and performing visual display.