157 results about "Relation classification" patented technology

The invention discloses a multi-modal classification method fusing a graph convolutional neural network and a capsule graph neural network, and solves the problem of how to apply an artificial intelligence technology to an appearance patent. The method comprises the following steps: coding a patent text sequence by utilizing BERT, segmenting semantic graph convolution by utilizing PCNN, constructing a dependency tree by utilizing grammar graph convolution, carrying out interactive attention and relationship classification, and finally carrying out image feature extraction through a capsule network. According to the method, the appearance patent classification technology fusing heterogeneous multi-modal information is researched, the feature extraction and fusion problems of multi-modal information such as characters, images and networks are solved, a heterogeneous multi-modal appearance design patent classification model is constructed, and the classification accuracy is greatly improved relative to pure text content. And meanwhile, an appearance design patent AI standard data set is constructed on the basis of the intellectual property hypergraph network, so that the blank of patent types in a traditional Lankyo classification system is filled, and the bottleneck of a traditional appearance patent classification method is broken through.

The invention discloses an entity relationship recognition method based on rotation position coding and a global pointer network, which comprises the following steps of: coding an input Chinese threat intelligence text by utilizing a pre-training language model fused with the rotation position coding, and generating context semantic representation with enhanced position perception capability; based on the context semantic representation, decoding all possible entity spans and types thereof in parallel in a two-dimensional grid space through a global pointer network to generate an entity set; for a target entity pair in the entity set, constructing a structured input sequence containing entity position information; processing the structured input sequence by using a double-attention coding mechanism; and based on the output of the double-attention coding mechanism, determining the relationship type between the target entity pairs through a relationship classification module. According to the method, precise decoding of nested entities and robust recognition of cross-language terms are realized through geometric space mapping and a dynamic boundary optimization mechanism.

To provide a method for accurately predicting a relationship between a particle diameter and a recovery rate of particles in particle classification using a classifier.SOLUTION: A classification result prediction method includes the steps of: acquiring a first learned model having machine-learned on the basis of first device conditions of a classifier and a result of fitting, with a function, a relationship between a particle diameter and a recovery rate of particles in classification of particles under the first device conditions using the classifier; and inputting second device conditions of the classifier to the first learned model and estimating, from an obtained output result, the relationship between the particle diameter and the recovery rate of particles under the second device conditions.SELECTED DRAWING: Figure 1

The invention provides a time sequence knowledge graph-based target action prediction method, which comprises the following steps of: acquiring multi-source data which comprises a plurality of entities; based on a relationship classification model, obtaining an association relationship between the entities, and completing redundancy check and alignment de-duplication of the association relationship, the relationship classification model being obtained by classification learning; after target entities in the multiple entities are determined according to the positions and the feature information of the entities, an association tree of association relationships among the target entities is dynamically constructed to complete construction of the time sequence knowledge graph; based on the time sequence knowledge graph, modeling and classifying the time sequence nonlinear incidence relation between the target entities by using a deep neural network to generate a target space evolution model; and predicting a behavior pattern and a motion track of the target entity based on the target spatial evolution model.

The invention discloses a lightweight scene graph generation method for an unmanned aerial vehicle platform, and the method comprises the steps: directly decoding a subject-object pair which may have a predicate relation from an image through designing a lightweight model based on a Transform encoder-decoder architecture; and adopting an integrated learning technology, re-sampling from an original training set to generate a plurality of sub-training sets, training a plurality of predicate relationship classifiers, and integrating and constructing a predicate relationship classifier with higher robustness. And integrating the obtained multiple groups of subject-predicate-object triples to generate a scene graph. The problem of real-time and efficient scene understanding in a resource limited environment is solved for the scene graph generation requirement of an unmanned aerial vehicle platform, the generated scene graph is rich in semantics and clear in structure, the intelligent sensing and decision-making capability of the unmanned aerial vehicle in a complex dynamic environment is remarkably enhanced, and the method has high practical value and popularization prospects.

The invention belongs to the technical field of artificial intelligence, and relates to a training method of a few-sample relation classification model and related equipment, and the method comprises the steps: constructing a business knowledge graph according to cleaning business data; performing data set division on the business knowledge graph to obtain an initial training set, a verification set and a test set; training the original language model according to the initial training set to obtain a pre-trained language model; constructing a seed relationship r and a seed sentence set Sr according to the verification set and the test set, and inputting the seed relationship r and the seed sentence set Sr into a sentence generator to obtain an optimal candidate sentence; expanding the seed sentence set Sr according to the optimal candidate sentence to obtain an expanded training set; and performing model training on the prototype network model according to the extended training set to obtain a target network model. According to the method, relation classification is effectively carried out under the situation of few samples and zero samples, the accuracy and generalization ability of the model are improved, the data annotation cost is reduced, and the method is suitable for knowledge graph construction and information extraction tasks in the field of electric power design.

The invention relates to the field of relation triple extraction, in particular to a relation triple joint extraction method based on information enhancement and bidirectional modeling. According to the method, through entity-to-relation and relation-to-entity double-branch collaborative modeling, semantic information is enhanced through double-branch potential information complementation: a bipartite graph matching method is combined with subjects and objects extracted by an entity extraction module, and relation classification is optimized through an entity boundary mask attention enhancement method; a potential relation extraction module is used for guiding subject and object entity extraction, and entity boundary information is utilized to crosswise mask attention of a large related area; the two branches filter redundant triads through a relation triad cutting module; combining bidirectional results and outputting a complete triple set; according to the method, a bidirectional interaction method is introduced to realize mutual enhancement of entities and relationships, and attention distribution is optimized in combination with entity boundary masks, so that the problem of high dependence on an initial extraction result caused by unidirectional modeling in traditional joint extraction is effectively solved.

The invention discloses an intelligent equipment fault relation extraction method based on multi-semantic knowledge interaction and dynamic pruning. Performing word segmentation and sentence segmentation on the original equipment fault text to obtain a statement set; obtaining a character-level word embedding representation set of the text by using a pre-training language model; extracting fault entity representations, obtaining fault type representations based on the constructed fault type knowledge base, constructing semantic association between the fault type representations, and generating a context feature representation sequence; calculating a correlation score between the enhanced semantic representation and an original text statement based on a dot product attention mechanism, dynamically selecting a core statement set, focusing the core statement, updating the semantic representation, and inputting the semantic representation into a relationship classification module to predict a relationship type between the entity pairs; and finally, outputting a named entity boundary and a relation label based on conditional random field decoding to obtain a final relation extraction result. According to the method, the ability of understanding the context is enhanced, and the fault relation extraction task under the complex context can be better processed.

The present invention provides a method and system for constructing a knowledge graph for mathematics courses, and relates to the technical field of knowledge graphs. The present invention solves the technical problem that the current relationship extraction method cannot accurately mine the entity relationship of mathematics courses, and realizes a relationship classification method by integrating sparse biaffine attention and adaptive threshold loss, overcoming the difficulties of extracting complex dependencies and overlapping relationships. At the same time, a hyperbolic space word embedding sparse projection model is used to predict the upper and lower relationships. The hyperbolic space has the ability to express hierarchical structures, can better predict the upper and lower relationships, and reduce the computational cost.

The invention provides a multitask event relation extraction method and system based on relative time analysis, and relates to the field of natural language processing. The method comprises the steps of obtaining to-be-analyzed text data; the text data are input into a pre-training language model ERNIE, and the understanding of the pre-training language model ERNIE on events and context semantics is enhanced in combination with a Masked Language Modeling task; constructing a multi-task learning framework based on relative time prediction; through the multi-task learning framework, jointly training an event relationship classification task and an event relative time prediction task, and optimizing model parameters of the pre-training language model ERNIE; and outputting the time relationship between the at least two events. And the difference between the classification task and the pre-training task is reduced, so that the relationship classification effect of the model on the events is improved.

This disclosure introduces a novel method and system for making a knowledge graph. A relation classification model is used to classify relationships between entities found in natural-language text. These entities become the nodes and the relationships between them become the edges in the knowledge graph. The entities are specific items that are relevant to the subject matter of the natural-language text. If the natural language documents are biomedical texts, the entities may be things such as chemicals, diseases, and genes. The relation classification model uses a transformer-based deep neural network architecture to understand the meaning of the text that contains the entities. The relation classification model also includes a classification layer that classifies the type of relationship between the entities found in the texts. With the knowledge graph, a user can efficiently receive answers to questions based on the aggregate knowledge found in many different documents.

The invention discloses a helicopter transmission system fault information extraction method based on RL-GPNet, and relates to the technical field of helicopter transmission system faults, and the method comprises the steps: carrying out the entity recognition based on a global pointer network, and effectively capturing the long-distance dependency relationship and complex semantic information in a fault description text through global normalization and a relative position coding mechanism; meanwhile, in combination with a PPO algorithm, triple generation is modeled into a multi-step decision task, an entity recognition and relation classification strategy is trained cooperatively, and error accumulation caused by task conflicts is relieved; good generalization ability is shown when long text entity overlapping and helicopter transmission system faults related to a large number of technical terms are processed, and effective technical support is provided for intelligent fault diagnosis of the helicopter transmission system; the method has important theoretical significance and application value for improving the intelligent level of helicopter equipment maintenance guarantee.

The invention discloses a large language model illusion detection method based on dynamic subgraph retrieval, relates to the technical field of model illusion detection, and aims to solve the problem of poor detection effect of a current illusion detection method. Comprising the following steps: acquiring and preprocessing a data set to obtain a training set and a test set; performing fine tuning according to the preprocessed data set to obtain a hop count predictor, a relation classifier, an entity classifier and a fact classifier; using cue words to extract an entity list from the question answer pair by means of a large language model; starting from entities in the entity list, extracting related evidence sub-graphs from the knowledge graph by utilizing a hop count predictor, a relation classifier and an entity classifier in a cooperative manner; and judging the correctness of the answer pair of the original question by using a fact classifier according to the obtained evidence sub-graph. According to the method, the evidence sub-graphs are dynamically retrieved through cooperation of a plurality of classifiers, and related sub-graphs of complex reasoning questions can be retrieved more accurately so as to judge illusion of question answer pairs.

The present invention is a method and device for filtering spam based on a large language model, and the method comprises the following steps: 1) email preprocessing to extract the content of received emails, including the body, attachments and metadata; 2) feature extraction and vectorization: converting the email content into a high-dimensional semantic vector through a large language model to capture the semantic information and contextual relationship of the email content; 3) classification and screening: using the large language model combined with a fine-tuned spam classifier to classify emails into spam and non-spam, and the classifier makes a comprehensive judgment based on contextual semantics, sending behavior patterns and metadata features; 4) feedback mechanism: introducing user feedback and model adaptive update mechanism to improve model performance through feedback learning. The present invention improves the accuracy and adaptability of spam filtering by introducing a pre-trained large language model.

The invention provides a scene graph generation method based on graph semantic understanding, which can be used in the financial science and technology field or other fields, and comprises the following steps: obtaining an initial environment image, and carrying out feature extraction and target detection on the initial environment image to obtain an initial category feature, a spatial feature and a visual feature of the initial environment image; performing knowledge embedding on the knowledge vector representation of the initial category feature and the visual feature to obtain a target category feature; based on the target category features, the spatial features and the visual features, obtaining context information and performing relation classification through a preset classification model to generate an initial scene graph; performing graph embedding on the initial scene graph, and performing similarity calculation to obtain a similar scene graph sorting list corresponding to the initial scene graph; and displaying the similar scene graph sorting list, and determining a target scene graph corresponding to the initial environment image. The invention further provides a scene graph generation device and equipment based on graph semantic understanding, a storage medium and a program product.

The invention relates to a few-sample relation extraction method and device based on a multi-knowledge enhanced prototype network. The method comprises the steps that a multi-knowledge enhancement prototype network composed of a semantic encoder, a multi-knowledge enhancement learning module, a double-contrast learning module and a relation prediction module is constructed, and the network adopts prompt learning to design a prompt template with entity information to activate knowledge in a pre-training language model; more accurate instance semantic representation can be obtained; meanwhile, two kinds of prior knowledge of multi-granularity entity types and relation description are introduced, and semantic representation of instances and prototypes is enhanced; a double-contrast learning module based on an instance and a prototype is designed, and category uniqueness and distinguishability of instance representation and prototype representation are learned from two different levels of the instance and the prototype, so that features of all relationships in the text can be fully captured and understood based on a small number of samples; and the accuracy of text entity recognition and relationship classification prediction is improved.

The invention provides an entity relationship classification method and related equipment, and the method comprises the steps: achieving the integration of an attention mechanism into entity relationship classification from a global range in a manner of processing an embedded vector of a target entity pair through a pre-constructed one-way gated loop network, obtaining an attention vector, and achieving the classification of the entity relationship. Secondly, in order to avoid the problem that when sentences are long and complex, the accuracy of entity relation classification is affected by noise caused by irrelevant words, potential keywords of local attention are recognized through a hard local mechanism algorithm or a soft local mechanism algorithm, the attention weight of the real keywords is improved, and the accuracy of entity relation classification is improved; according to the method, the attention weight of the noise word is reduced, so that a real keyword can be screened out, effective entity relationship classification can be realized on the basis, and the accuracy of a relationship classification result of the target entity pair can be improved.

The invention discloses a neuroscience literature processing method and system for constructing a brain connection knowledge graph. The method comprises the following steps: training an entity recognition model; training a relation extraction model; literature data acquisition and standardization processing; entity and relation extraction; performing entity standardization; construction and storage of the knowledge graph; and application analysis of the knowledge graph. According to the method, the entity boundary recognition and relation classification accuracy is remarkably improved, the problems of inaccurate boundary prediction, wrong relation recognition and the like are remarkably relieved, and standardization and linking of entities are achieved by introducing a fuzzy matching and word vector similarity calculation method; meanwhile, reasoning and visual analysis of the brain region connection relation are supported, application scenes such as document retrieval, specific brain region input and output loop analysis and author community structure recognition are covered, and comprehensive and systematic knowledge support is provided for nervous system structure and function research.

The invention discloses a relation perception type two-channel entity relation extraction method. The method comprises the following steps of S1, encoding sentences through an encoder; s2, inputting the sentence vectors into a potential relation extractor, and extracting potential relation features; s3, inputting the potential relation characteristics into a dual-channel extractor, and extracting from two channels of head entity priority and tail entity priority respectively; and S4, judging the association among the subject, the relationship and the object through the bisimulation network, aggregating the entity pairs extracted by the two channels, and performing relationship classification on the entity pairs. The problems that relation recognition lacks entity semantic support and error prediction is irreversible in a relation priority type joint extraction method can be solved.

The invention discloses a visual scene graph generation method based on differentiable fuzzy logic reasoning, and solves the technical problems of semantic fuzziness, logic common sense deficiency and the like during long tail relation processing in the prior art. The method comprises the following steps: inputting a training image into a target detection module, and respectively outputting corresponding high-dimensional geometric embedding vectors to a relation classifier and a fuzzy mapping layer; outputting a visual prediction branch by the relation classifier; meanwhile, the fuzzy mapping layer outputs a fuzzy membership degree vector; the logic tensor reasoning module is combined with a common sense rule in an external knowledge base, simulates a logic reasoning process by utilizing a differentiable logic operator, and outputs a logic reasoning branch of which the relation of each pair of objects meets a preset logic rule in the training image; a gating residual fusion module performs weighted fusion on the visual prediction branch and the logical reasoning branch to generate a scene graph triple of the training image; and finally, training the network model, inputting a test image into the trained model, and outputting a corresponding visual scene graph.

The invention belongs to the technical field of approximate semantic matching of texts, and particularly relates to a rule retelling matching method and system based on deep learning and cosine similarity matching. Comprising the following steps of: 1, constructing a graph structure of a text, expressing text information as a graph node mode, and expressing a relationship between vocabularies by using edges of a graph; 2, introducing a nuclear power professional vocabulary library; 3, modeling context vocabulary correlation by a language knowledge attention model, iteratively updating word representation and semantic representation, and fusing useful information of the word representation and the semantic representation; and 4, fusing word representation into a character level, matching two character sequences by using a bilateral multi-view matching mechanism, and predicting a relationship between two sentences by using a relationship classifier by using a sentence vector as an input. The method has the beneficial effects that the problem that accurate matching is difficult due to the fact that the repeated text and the original procedure text are the same in semantics and different in vocabularies during personnel retelling can be solved.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an entity and relation joint extraction method, which comprises the following steps of: acquiring audio data, extracting voice features, compressing and reducing dimensions of the voice features, and decoding the compressed voice features to generate a linearized text sequence; and performing entity recognition and relationship classification on the text sequence by using a pre-trained recognition model to generate triple information containing entities and relationships. According to the method, the intermediate conversion step from the audio to the text is omitted, the semantic information is directly extracted from the audio data, information loss possibly occurring in the modal conversion process is reduced, and the processing efficiency and accuracy are improved. Besides, entity recognition and relation classification are carried out on the text sequence through the pre-trained recognition model, it is ensured that key information can be efficiently extracted from a large amount of audio data under the condition of low cost, and combined recognition of entities and relations is achieved.

The present application relates to the technical field of relation classification, and particularly relates to a small sample relation classification method based on a graph neural network, which proposes a small sample relation classification method based on graph learning for a relation classification task containing only a small amount of samples, uses a graph neural network in graph learning to construct a small sample relation classification method, uses a prototype network to further improve the effect of relation classification, simultaneously imitates the ability of humans to quickly learn new knowledge by using past learned knowledge through meta learning, reduces the dependence on a large amount of labeled data, and the model learns the generalization ability of adaptive learning between different categories through training, so that the method does not need to be changed when facing a new category for testing, and the learned generalization knowledge can adapt to the classification task of the new category.

The invention provides a document-level relation extraction method for fusing subgraphs and displaying a constructed reasoning path, and belongs to the technical field of natural language processing. The method comprises the steps that an input text sequence is converted into a word vector sequence through an encoder; constructing a heterogeneous graph comprising entity nodes, mention nodes and sentence nodes through a document graph construction layer; explicitly defining three reasoning paths in sentences, between sentences and integration; extracting a sub-graph from the periphery of the target entity based on the document graph and the reasoning path, and reasoning by applying an R-GCN network; global encoder feature information and local subgraph feature information are sent to a fusion relation classification layer for relation distribution probability prediction; and optimizing a weighted adaptive loss function, and dynamically adjusting loss contribution degrees of different types of samples. According to the method, the reasoning ability for complex relations, the long-distance dependence capture effect and the accuracy of long-tail relation recognition are improved, and meanwhile, the accuracy, efficiency and generalization ability of document-level relation extraction are remarkably improved.

The application provides a relationship extraction method and system based on entity replacement and floating mark, and relates to the field of natural language processing.The method comprises the following steps: performing entity replacement and floating mark processing on the original text to obtain a detection text; performing language feature extraction processing on the detection text to obtain a feature vector; performing vector splicing processing and language feature extraction processing on the feature vector to obtain an entity relationship score vector; performing probability distribution prediction processing on the entity relationship score vector to obtain a category probability distribution graph; and performing decoding processing on the category probability distribution graph.The entity pair of the relationship to be extracted is replaced with a fixed mark in the application, so that the expression of the relationship is more general, and the relationship classification error caused by the name interference of the entity pair is solved; meanwhile, special representations are added before and after the entity pair, and the floating mark is marked at the end of the sentence, so that the problem of poor model generalization performance caused by insufficient representation of entity information in the relationship extraction process is solved.

The invention relates to a few-sample relation classification method integrating knowledge distillation and efficient parameter fine tuning. According to the method, a few-sample relation classification model comprising a mixed prompt input module, a coding module, a relation classifier and a consistency discriminator is constructed. According to the model, a parameter efficient fine tuning strategy is adopted, only a small number of trainable virtual marks in mixed prompt input need to be optimized, meanwhile, all other model parameters are frozen, and the training efficiency is remarkably improved. Moreover, the model constructs a teacher-student framework with consistency constraint through a knowledge distillation technology, can effectively extract fact knowledge in the pre-training model, and obtains more stable relation representation from the perspective of memory enhancement. Besides, the model can further refine and extract more accurate prototype representation and reduce noise interference by performing weighted average on the relationship representation of the support set instances, and finally can improve the classification prediction accuracy of the query set instances in a few-sample relationship classification task.

The invention relates to the technical field of natural language processing and mapping knowledge domain, and particularly discloses a mapping knowledge domain relation extraction method fusing a syntactic structure and a domain rule. The method aims at solving the problems of semantic understanding deviation and insufficient domain knowledge utilization in vertical domain relation extraction. The method comprises the steps that dependency syntactic analysis is conducted on a text, a syntactic dependency adjacency matrix is constructed, and meanwhile a rule adjacency matrix is generated based on domain rule base matching; carrying out weighted fusion and filtering on the two types of matrixes to obtain an enhanced adjacent matrix; inputting the matrix and a text vector into a graph convolutional network fused with a dependency type attention mechanism, and learning to obtain a node enhancement representation; and finally constructing an entity pair feature vector to finish relationship classification. Through explicit fusion of interpretable syntactic rules and domain priori, the accuracy and robustness of relation extraction in professional fields such as a power distribution network are improved. Experiments show that the model relation extraction F1 value reaches 86.07% and is improved by 1.31% compared with a baseline model with the optimal performance.

The present invention discloses a method and device for entity-relationship joint extraction based on a lightweight self-attention mechanism. The method comprises obtaining target sentence data and inputting the target sentence data into an entity classification model; classifying all subsequences of the target sentence data using the entity classification model to obtain entity sequences and non-entity sequences; obtaining any pair of entity pairs in the entity sequence and inputting the entity pairs and the word combinations between the entity pairs into a relationship classification model; and generating entity-relationship joint classification results using the entity-relationship joint extraction model; wherein the entity-relationship joint extraction model includes a Bert encoder, an entity classification model, and a relationship classification model. The present invention has low complexity, can improve the performance of entity-relationship models, and can be widely applied in the field of artificial intelligence technology.

The invention belongs to the technical field of natural language processing, and particularly relates to an entity relation joint extraction method oriented to industrial knowledge graph construction and causal reasoning driving. The method comprises the steps of obtaining a to-be-processed industrial field unstructured text and inputting the to-be-processed industrial field unstructured text into a pre-training language model for shared coding to obtain a shared feature sequence; processing the shared feature sequence by adopting a dual-channel gating module to obtain an entity feature sequence and a relation feature sequence; fusing the entity feature sequence and the relation feature sequence by adopting a causal inference module to generate a causal enhanced feature sequence; inputting the causal enhancement feature sequence into a joint decoder for entity boundary identification and relationship classification to obtain an entity relationship triple; according to the method, the task features are separated, confusion is eliminated through causal reasoning, the accuracy and robustness of entity relation extraction in a complex industrial text are remarkably improved, and particularly the effect in the aspects of processing nested entities and multi-task feature interaction is prominent.

The application discloses a low-resource biomedical relation extraction method based on adaptive prompt learning and constraint enhancement, aiming at problems of low-resource annotation scarcity, poor adaptability of artificial prompts, and illegal output triplets, and constructs a double-branch training architecture of few-shot and full-shot; a VAE is used to realize soft prompt vector coding, compression and reconstruction through an adaptive prompt generation module, a gate mechanism is used to dynamically adjust the fusion proportion of prompts and texts, and context representation learning is completed through SciBERT; an entity extraction is realized through a span extraction based on a relation recognition module, a multi-label relation classification is used to predict the relation probability of entities; and a constraint enhancement decoding module filters illegal combinations and removes redundant triplets through a relation type constraint mask, entity-relation score weighted fusion, relation number prediction and pruning strategy. The application significantly improves the extraction accuracy in a low-resource scene, the output triplet structure is legal and consistent, and the application is suitable for biomedical professional texts and small-area field data scarce scenes.