73 results about "Named-entity recognition" patented technology

The application provides a project declaration content compliance review method and system combining named entity recognition and large models, and relates to the technical field of natural language processing. First, a project declaration full-cycle document set is obtained, including declaration documents, contracts, mid-term examination and acceptance documents, a fine-tuned named entity recognition model is used to extract structured entity triples, and a data set containing logical association relationships is constructed. Then, a compliance review prompt engineering instruction set is constructed, structured entity triples are input into a large language model for multi-document entity alignment and consistency verification, numerical evolution, semantic deviation and logical contradiction features are identified, and identification results are generated. Finally, a traceable review evidence chain set is constructed according to the identification results, the source of the problem is accurately located, and the association with the compliance rules is clarified. The application improves the review efficiency, accuracy and systematicness.

This invention relates to the field of named entity recognition technology, and discloses a multimodal named entity recognition method and apparatus based on deep interaction of text and image features. The method includes: acquiring original text and an original image; extracting multimodal features from the original text and the original image respectively to obtain a text feature sequence and an image feature sequence fused with multi-scale features; performing cross-modal deep interaction fusion on the text feature sequence and the image feature sequence to obtain an interacted multimodal feature sequence; performing context-aware adaptive gating fusion on the interacted multimodal feature sequence to obtain a fused feature sequence; and performing conditional random field sequence decoding on the fused feature sequence to obtain an entity label sequence. This invention achieves deep bidirectional interaction and dynamic adaptive fusion of multimodal information, effectively solving the text ambiguity problem and improving the accuracy of named entity recognition.

The application discloses a cross-domain named entity recognition method based on an autoregressive model, and comprises the following steps: S1, encoding an input sequence; S2, encoding a label through a label encoder; S3, obtaining label background information; S4, obtaining label context information; S5, connecting the label background information to the input sequence and connecting the label context information to the predicted named entity label as final label perception information z i , and finally obtaining a final sequence representation u; the application provides a cross-domain named entity recognition method based on an autoregressive model, which improves the relationship between a source text and its named entity label, improves the portability of label information, and helps the model to promote domain adaptation.

This application relates to a method, apparatus, electronic device, and storage medium for detecting abnormal server behavior, applied in the field of network security technology. The method includes: real-time acquisition of system call sequences during server process execution; dividing the system call sequence into N runtime phase subsequences, and further dividing the runtime phase subsequences corresponding to the target process's runtime phases into process activity subsequences; segmenting the system call sequence into multiple unidentified subsequences; inputting each unidentified subsequence into a process semantic abstraction model to obtain the named entity recognition result corresponding to each system call in the unidentified subsequence; aggregating and overwriting the named entity recognition results of multiple unidentified subsequences to obtain the named entity recognition result corresponding to each system call in the system call sequence; if unidentified named entities are included, determining that the system call sequence is abnormal, and sending an alarm message. This application can extract process behavior, facilitating expert analysis of abnormal events.

This invention discloses an AI-based integrated system for intelligent classification, storage, and retrieval of archives. The system includes: an archive classification module that inputs archive text into a MacBERT model for classification; a category template generation module that determines the target entity field identifier set based on archive category labels; a named entity recognition module that generates structured archive metadata; a multi-view encoding module that generates view pooling vectors; a multi-view interactive sentence vector generation module that generates sentence vector representations of target archives; a vector index library module that stores sentence vector representations of target archives; a structured database module that stores structured archive metadata; and an archive retrieval module that outputs retrieval results by combining structured archive metadata during the retrieval phase. This invention is applicable to automated management and semantic-level intelligent retrieval scenarios for large-scale government and enterprise archives.

The application provides an intelligent manufacturing method and system of an office chair, relates to the technical field of machine industry vision, acquires a user order containing a natural language text describing product requirements, extracts manufacturing feature parameters for describing product geometry and appearance from the natural language text by using a named entity recognition method based on a pre-trained language model, generates a three-dimensional digital model of the product based on the manufacturing feature parameters, integrates geometric data and material attribute data of the three-dimensional digital model, constructs a digital twin corresponding to a physical production line in a virtual environment, plans a manufacturing process path of the product based on the digital twin, configures corresponding visual sensing units and detection task parameters for each manufacturing process in the manufacturing process path, and acquires multi-modal visual data with time stamps and workstation identifiers in each physical manufacturing process through the visual sensing units configured for each process.

The application discloses a network security vulnerability knowledge graph construction method based on HPO-BiLSTM-CRF, which comprises the following steps: collecting public vulnerability data from related databases in the network space security field, preprocessing, and constructing a data set; analyzing and extracting existing data source feature information, and constructing a network security vulnerability domain ontology model CSVDO; based on the optimized bidirectional long short-term memory network and the conditional random field fusion model HPO-BiLSTM-CRF, realizing named entity recognition and relationship extraction; adopting an integrated entity alignment method for knowledge fusion, matching different instances of the same object in different ontologies based on an improved similarity measurement algorithm, and constructing a knowledge graph; carrying out knowledge graph embedding, storing the result into a graph database, and completing knowledge graph construction and graphic visualization. The application can improve the efficiency of entity recognition and relationship extraction in network security vulnerability knowledge, and has the advantages of high efficiency and high accuracy compared with other graph construction methods.

The application discloses a multi-source heterogeneous geological data desensitization method and device, including the following steps: according to the input multi-source heterogeneous geological data, performing named entity recognition, and extracting geological entity data in the multi-source heterogeneous geological data; linking the geological entity data and the pre-input geological knowledge graph to obtain reasoning data; evaluating the reasoning data through a dynamic sensitivity grading evaluation model to obtain the sensitivity grading of the reasoning data with different sensitivities, and determining a sensitive target; performing geometric remodeling on the sensitive target, simulating the tectonic movement of the sensitive target, performing constraint and topological anti-flipping through a Jacobian determinant, constructing a Lagrange lock of key points, and obtaining spatial deformation data; and performing attribute desensitization and picture-text collaborative desensitization on the spatial deformation data to obtain desensitization data. The technical scheme can keep the real geological data hidden while maintaining a high degree of logical consistency with the original data.

The application discloses a kind of multi-modal named entity recognition methods for multi-graph scene, belong to computer vision and multi-modal information extraction technical field, including: constructing entity cross-graph candidate visual region set and determining initial visual support weight;Based on entity type label, entity condition multi-instance supervision is carried out, and false matching is inhibited by non-entity negative sample constraint;Based on the entity type prediction result of set, visual region disturbance is carried out, and contribution degree and discriminativeness are determined according to target type prediction score change;Based on contribution degree and discriminativeness, visual reconstruction is guided by contribution, and support maintenance ability is determined according to target type prediction score retention degree;Further, visual contribution calibration score is generated to calibrate aggregation, and calibrated cross-graph entity level visual representation is directed to text representation, to obtain multi-modal named entity recognition result.The method can improve the reliability and accuracy of entity recognition in multi-graph scene.

A method and apparatus for Chinese medical named entity recognition based on multi-level adaptive semantic enhancement, the method comprising: (1) representing Chinese text as T={C1、C2、···、C N}, construct character C i Features, including character features, boundary features, radical features, and pinyin features; (2) Character-level C is generated through the ERNIE-Health pre-trained model. i The features are transformed into vector representations, including character feature vectors e. c Boundary eigenvector e b , radical feature vector e r Pinyin feature vector e p (3) Input the four character-level features into the character-level adaptive semantic enhancement module. Use convolutional layers to compress the character features, perform nonlinear transformation through gating mechanism and ReLU activation function, dynamically adjust semantic weights, and use multilayer perceptron for decompression to obtain enhanced character-level features; (4) Input the enhanced character-level features into the sentence-level adaptive semantic enhancement module, and adaptively learn the contribution of different characters in the sentence through compression and decompression mechanism; (5) Input the enhanced features after multi-level adaptive semantic enhancement module into BiLSTM-CRF module for label prediction. This invention can better capture semantic differences in context, solve the limitations of existing methods in feature weight allocation, and improve the overall performance of CNER task.

The application discloses a named entity recognition model and method for Qin Dynasty bamboo text, adopts a two-stage training framework of parameterization and non-parameterization cooperation: in the first stage, parameterized fine-tuning is carried out by using a Qin Dynasty bamboo text basic semantic encoder to obtain a basic model adapted to the Qin Dynasty bamboo text context; in the second stage, an external key-value index database is constructed through a static feature extractor, and a self-adaptive fusion is carried out on a parameterized prediction distribution and a non-parameterized retrieval distribution by using a retrieval enhancement fusion network, wherein a confidence threshold network dynamically adjusts a fusion weight, high confidence depends on parameterized knowledge, and low confidence depends on external retrieval for completion, and finally, a global optimal BIO label sequence is output through a structured sequence decoder, effectively solving the problems of long-tail entity recognition difficulty under a low-resource historical corpus and semantic rupture caused by missing of a damaged text context.

An information retrieval method includes: obtaining a query text used to trigger information retrieval, and performing named entity recognition on the query text to recognize an entity word from the query text as a retrieval entity word; calculating an association degree of each candidate entity word relative to the retrieval entity word based on a graph; determining a predetermined quantity of candidate entity words with a highest association degree relative to the retrieval entity word as associated entity words, and calculating an association degree of each candidate document relative to the query text based on association degrees of the associated entity words relative to the retrieval entity word; and determining a predetermined quantity of candidate documents with a highest association degree relative to the query text as associated documents, and determining the associated documents as an information retrieval result corresponding to the query text.

This invention discloses a privacy-graded data protection method for intelligent detection of big data. In the field of information security technology, the method first segments the original data text into words, extracts high-dimensional semantic features of each word based on a word embedding model and a context-aware encoder, and calculates dynamic sensitivity scores using a pre-trained deep neural network. Based on this, the words are divided into five increasing sensitivity categories. A differentiated privacy budget strategy is adopted, and intensity-adapted Laplacian noise is injected based on a differential privacy mechanism to generate privacy-preserving word features and construct privacy-protected text. This allows for direct input into a big data intelligent detection system to perform intelligent analysis tasks such as named entity recognition, keyword extraction, relation extraction, and sensitive content identification. Accurate detection is achieved while ensuring privacy security, balancing privacy protection strength with data usability. This method is suitable for privacy computing and intelligent analysis needs in large-scale data scenarios.

The application relates to a cross-domain few-shot named entity recognition method based on coupling of Gaussian distribution and Wasserstein metric, which comprises the following steps: obtaining source domain text data and target domain text data to be processed, and respectively performing data preprocessing; obtaining deep hidden layer state features of each word element; generating a multivariate Gaussian distribution representation with position perception capability; constructing a class Gaussian prototype representing the distribution features of each entity category; and finally outputting the predicted entity category of the text sequence to be measured. By establishing the generation condition of the covariance on the basis of the semantic mean position, the application realizes position-perception uncertainty modeling, so that entities close to the fuzzy decision boundary naturally obtain greater variance, accurately cover the effective semantic radius, and even in the case that the feature distribution of the source domain and the target domain is not overlapped, the application can still provide smooth geometric transmission cost, ensures the stability of optimization, and greatly improves the accuracy of cross-domain named entity recognition.

The application discloses a model training and recognition method and device for named entity recognition and a storage medium. The training method comprises the following steps: labeling data samples in a training set based on a rule base to obtain first labeled names of the data samples; training an initial NER model based on the data samples and corresponding first labeled names; predicting the data samples in the training set based on the trained NER model, clustering each data sample based on a predicted entity name and an intermediate result, and determining second labeled names of each data sample based on a clustering result; and continuing to train the NER model based on the data samples and corresponding second labeled names until the clustering result meets a convergence condition, so that a trained NER model is obtained. The automatic labeling of samples can be realized by using the rule base, the prediction result can be calibrated and the subsequent model can be optimized and trained based on clustering, and the training effect of the NER model can be effectively improved.

The application discloses an engineering material intelligent inquiry matching method and system based on natural language processing. The method comprises the following steps: obtaining an inquiry request text input by a user; performing named entity recognition by using a pre-trained language model in the field of engineering cost to extract key entities such as material name and specification model; performing semantic normalization processing through a domain ontology knowledge base; encoding the normalized conditions into vectors and combining structured fields to perform hybrid retrieval; performing adaptive statistical outlier filtering on candidate prices; calculating a confidence score according to the data volume, timeliness, semantic matching degree and data source label by using Bayesian confidence estimation, and outputting a recommended result in the form of a price interval; and receiving user feedback to continuously optimize the system. The application realizes semantic-level understanding through field customization NLP technology, overcomes the lexical gap problem, provides multi-dimensional reliable price reference, supports batch automatic processing, and significantly improves the efficiency and accuracy of engineering material inquiry.

The application provides a Chinese named entity recognition method based on an attention mechanism, an electronic device and a storage medium, which comprises the following steps: inputting a text to be recognized into an embedding layer to obtain a word vector; using a Transformer encoder to extract features of the word vector to obtain a first context feature; using a Bi-LSTM model to extract features of the word vector to obtain a second context feature; fusing the first context feature and the second context feature to obtain a fused feature; and decoding the fused feature to obtain Chinese named entities corresponding to the text to be recognized. The Chinese named entity recognition method based on the attention mechanism realizes deep fusion of global semantic information and directional information. In order to obtain more context information and solve the problem of polysemy, a RoBERTa-wwm pre-training model is used as a character-level embedding, so that the model recognition effect is improved.

This invention provides a method for constructing and reasoning a ship navigation knowledge graph based on multi-source heterogeneous data, involving the intersection of artificial intelligence and maritime technology. The method includes: Step S1, establishing a ship navigation knowledge model; Step S2, acquiring and preprocessing multi-source heterogeneous data; Step S3, performing named entity recognition on the preprocessed data based on a BiLSTM-CRF hybrid model incorporating domain dictionary features; Step S4, extracting entity relationships from the entities identified in Step S3 using a pre-trained BiLSTM hybrid model incorporating interactive attention mechanisms; Step S5, fusing knowledge from the entities and entity relationships extracted in Steps S3 and S4 to construct a preliminary knowledge graph; Step S6, performing link prediction on the preliminary knowledge graph based on an RGCN model incorporating temporal constraints and rule logic to achieve knowledge completion and reasoning. This invention improves the ability to respond to risks in complex navigation scenarios.

The present application relates to a kind of general english tweet preprocessing method and computer equipment, belong to data processing technical field;Solved the problem that a large number of subjective words and non-standard morphemes exist in english tweet, affect tweet preprocessing result and named entity recognition performance;The method of the present application includes: based on multiple field english text, subjective word table is obtained by construction;The non-standard morpheme in the english tweet to be processed is carried out semantic reduction and information extraction, and the tweet text after preprocessing is obtained;Based on the tweet text after preprocessing, double stack structure is constructed to extract clause;Based on subjective word table, using syntax dependency analysis model and tree parent-child level structure, the tweet text after preprocessing is carried out named entity extraction, and the named entity recognition result of english tweet is obtained;The preprocessing result of english tweet is obtained by outputting the tweet text after preprocessing, the clause contained in english tweet and the named entity recognition result of english tweet.

This invention discloses a method and apparatus for named entity recognition in the construction of a multimodal knowledge graph. The method includes: constructing a multimodal graph based on text and image information, the multimodal graph including text nodes, visual nodes, and edges connecting different nodes; inputting the multimodal graph into multiple sequentially connected fusion layers; for each fusion layer, performing intramodal fusion on each text node and each visual node input to that fusion layer to obtain text nodes and visual nodes fused with contextual semantic information; performing cross-modal fusion on the fused nodes to obtain cross-modal fused text nodes and visual nodes; using the cross-modal fused nodes as input to the next fusion layer until the text node features and visual node features output by the last fusion layer are obtained; fusing the text node features output by the last fusion layer with the original text features output by BERT; and performing CRF decoding on the fused features to obtain the named entity recognition result. This application enables full interaction between image and text information and eliminates visual noise interference in the image.

The application discloses a kind of based on large language model's cascade commonality question mining method, it is related to artificial intelligence and natural language processing technical field.The steps include: S1, using small language embedding model to carry out feature vector expression to text question, reserve the first second quantity of coarse-grained commonality question with the highest number of sub-problems;S2, using large language embedding model carries out feature vector expression to the original text question, reserve the first fourth quantity of fine-grained commonality question with the highest number of sub-problems;S3, using large language general model carries out summary induction, obtains the fifth quantity of commonality question;S4, using large language general model carries out named entity recognition, and the commonality question is sorted according to importance degree.The application can mine and discover commonality question in text with higher precision and faster speed, can also effectively utilize the deep learning characteristics of large language model, to reduce the dependence on a large number of computing resources.

This invention belongs to the fields of artificial intelligence and natural language processing, specifically relating to a method and system for named entity recognition in biomedical text based on deep learning. The method includes acquiring training data of biomedical text labeled with genomic variant entities and augmenting it to obtain augmented data; segmenting the augmented data using an improved word segmentation and labeling method to obtain a word segmentation sequence; extracting features from the word segmentation sequence using a BioBERT layer to obtain a word vector sequence; inputting the word vector sequence into a stacked BILSTM network to extract text position information to obtain a feature vector sequence; using an improved scoring function in the attention layer to obtain semantic features of the feature vector sequence; and feeding the semantic features into four task modules, calculating task losses, and backpropagating to train the model. This invention's data augmentation method effectively solves the problem of data scarcity and proposes an improved word segmentation and labeling method to segment the augmented data, alleviating the problem of label sparsity.

This invention relates to a named entity recognition method based on a pre-trained language model and a progressive convolutional network, comprising the following sequential steps: encoding natural language based on the pre-trained language model to obtain a representation set LS; inputting the representation set LS into a progressive convolutional network module, and using the progressive convolutional network module to progressively fuse the encodings of adjacent layers from low to high levels to obtain an aggregated distributed representation AR that integrates the features of all layers of the pre-trained language model. c ;Utilizing the CRF model, i.e., Conditional Random Field, to decode and aggregate distributed representations (AR) c This invention achieves named entity recognition. Instead of introducing external knowledge or operations to enhance entity information and improve named entity recognition accuracy, it focuses on the results obtained using the available computing power. It utilizes the proposed progressive convolutional network module to extract the full-layer representation of the pre-trained language model, overcoming the deficiency of insufficient information mining from the pre-trained language model and reducing the complexity of introducing external data for computation.

Disclosed are a Nested Named Entity Recognition method based on part-of-speech awareness, system, device and storage medium therefor. The method uses a BiLSTM model to extract a feature of text word data in order to obtain a text word depth feature, and each text word of text to be recognized is initialized into a corresponding graph node, and a text heterogeneous graph of the text to be recognized is constructed according to a preset part-of-speech path, the text word data of the graph nodes is updated by an attention mechanism, and the features of all graph nodes of the text heterogeneous graph are extracted using the BiLSTM model, and a nested named entity recognition result is obtained after decoding and annotating. The present disclosure can recognize ordinary entities and nested entities accurately and effectively, and enhance the performance and advantages of the nested named entity recognition model.

This application provides a method and related apparatus for dynamically constructing a medical knowledge graph based on ontology-guided named entity recognition and multi-hop graph reasoning. The method includes: S1. acquiring medical text and loading a medical ontology; S2. performing ontology-guided named entity recognition based on the entity type hierarchy constraints of the medical ontology; S3. performing graph node fusion on the entity set based on a cross-sentence reference resolution algorithm; S4. generating a candidate set of relations for graph node pairs based on the relation type hierarchy of the medical ontology; S5. constructing an initial graph based on a graph neural network and performing multi-hop graph reasoning to infer implicit medical relations; S6. dynamically updating the medical knowledge graph based on a temporal window, outputting a medical knowledge graph containing multi-hop reasoning paths. This application also provides related apparatus corresponding to the method, including devices, electronic devices, computer-readable storage media, and computer program products.

This invention discloses a hybrid named entity recognition method for the ship polishing field based on entity structure differences, comprising the following steps: using a multi-dimensional rule routing engine to identify structurally stable entities in the text, determining the category of the corresponding structurally stable entity and its position information in the text; using a deep learning-based named entity recognition model to identify semantically dependent entities in the text; the deep learning-based named entity recognition model is trained on a structured text corpus for the ship polishing field; the recognition results of the multi-dimensional rule routing engine and the recognition results of the deep learning-based named entity recognition model are fused to obtain a fused recognition result; structurally stable entities and semantically dependent entities are classified based on the structural stability and semantic dependence of entities in the text expression, combining knowledge of the ship polishing field and the ship polishing process.

This invention discloses a hierarchical collaborative method and system for secure reasoning using a large language model, belonging to the field of secure reasoning technology for large models. The method includes: on the client side, firstly, named entity recognition is performed on the original document, and sensitive proper nouns are replaced with secure generalized descriptors based on entity type and differential privacy mechanisms, generating an intermediate document and entity type metadata; subsequently, the intermediate document undergoes lexical-level differential privacy semantic perturbation to generate a perturbed document; the perturbed document is sent to a cloud-based large language model to obtain the initial generated text; finally, on the client side, a local lightweight model is used to generate high-quality final text based on entity type metadata, context, and the initial text returned from the cloud. This invention, through a hierarchical collaborative privacy protection framework and under provable differential privacy protection, solves the problem of balancing proper noun protection and generation quality, and is suitable for secure reasoning scenarios involving sensitive texts in finance, healthcare, and other fields.

The application discloses a kind of structured knowledge extraction method, system, equipment and storage medium of automobile software design document, the method includes: to automobile software design document is carried out format conversion and area division, obtains structured data block set;Through sequence mark model, named entity recognition and type mark are carried out to text type data block, and obtain entity type label list;According to preset automobile software relationship rule, generate multiple groups of entity candidate relationship pairs according to entity type label list;According to text type data block, relationship extraction is carried out to each group of entity candidate relationship pairs by graph neural network, to generate relationship triple set;Entity type label list, relationship triple set, the structured information in text type data block is stored.The present application is sequentially linked by the steps of file analysis, entity recognition, relationship extraction and data storage, effectively solves the problem of intelligent extraction structured knowledge from automobile software multi-modal design document.

This application discloses a method, apparatus, and electronic device for entity recognition. The method includes: receiving text to be recognized input from an edge device; determining a target model corresponding to the text to be recognized from a named entity recognition model deployed on the edge device, wherein the named entity recognition model is obtained by adjusting the mask attention mechanism and feature output path of a generative model with fewer than a preset threshold of parameters; and using the target model to recognize the text to be recognized to obtain an entity recognition result. This application solves the technical problem of limited performance of named entity recognition in edge environments using language models with small parameters.