118 results about "Relation classification" patented technology

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.

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 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.

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 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 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 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.

The present invention relates to the field of natural language processing technology, and discloses an information extraction method and system based on a multi-task and GlobalPointer model, comprising the following steps: constructing an information extraction model including an encoder, a GlobalPointer model, and a classifier; performing multi-task training on the information extraction model using a training dataset including a named entity recognition dataset, a relationship extraction dataset, and a triple extraction dataset; inputting text into the trained information extraction model and encoding it into a vector representation through the encoder; extracting all spans in the vector representation through the GlobalPointer model and converting the spans into entity vectors; extracting the relationships between the entity vectors using the GlobalPointer model; classifying the entity vectors through a classifier; and outputting a triple data extraction result by integrating the entity vectors, relationships, and classification results. The present invention solves the problems of low reasoning efficiency and high computational consumption in the prior art, and has the characteristics of being able to improve label utilization and being suitable for massive data processing.

The examples of the present disclosure provide a method and apparatus for classifying an entity semantic relation, a model training method and apparatus, an electronic device, and relate to the field of text identification technology. A first entity and a second entity in a corpus is determined and obtained. According to a first position distance between each word in the corpus and the first entity and a second position distance between each word in the corpus and the second entity, a feature vector corresponding to each word is obtained. Then, feature vectors corresponding to all words in the corpus are combined to obtain a model input vector corresponding to the corpus. Then an entity semantic relation type corresponding to the corpus is obtained by using the model input vector corresponding to the corpus an input of the entity semantic relation classification model.

The present invention discloses a method and system for jointly extracting events and event relationships based on dynamic graph propagation. The present invention constructs a multi-level dynamic graph structure and introduces a gate mechanism to realize the dynamic transmission and joint update of information between event internal elements and events, thereby realizing end-to-end collaborative learning of subtasks such as event trigger word recognition, argument recognition, role classification, event type determination and event relationship classification. The present invention simultaneously completes the prediction of event elements, event types and event relationships through dynamic graph propagation, effectively captures contextual dependencies, and has significant advantages in processing nested events and long-distance event relationships. It effectively improves the overall robustness and structural modeling capabilities of the event extraction system and can be widely used in financial public opinion analysis, medical event tracking, judicial case reasoning and other fields.

The invention discloses a work order full-process electronic management method and system based on a knowledge graph, and relates to the technical field of work order management, and the method comprises the steps: defining the relationship between core entities and entities in the field of work order management to obtain a core entity relationship chain, and determining the attribute of each entity to construct a field ontology graph; dividing structured data and unstructured data, and respectively preprocessing the structured data and the unstructured data to obtain an entity-relation-attribute chain; constructing an initial knowledge graph, and connecting the initial knowledge graph with a work order management system after the initial knowledge graph is servitized; the method comprises the following steps: integrating work order initiation channels by using a system, collecting data, classifying the data according to an initial atlas entity-relationship, matching an optimal resource, and optimizing a standard atlas of the atlas; generating an optimal solution and a knowledge pushing list in combination with the standard map, and recycling work order processing flow information; and archiving the work order and carrying out secondary optimization on the atlas to obtain a preferred standard atlas. The method has the advantages of realizing work order whole-process intelligentization, improving the processing efficiency and ensuring knowledge precipitation and reuse.

The application provides a kind of entity and relation serial extraction method for text data.First, the text is preprocessed, and its word vector is extracted, then the word vector is input into the text entity extraction model BiLSTM-softmax to obtain the text with entity pair, then the text with ambiguity is disambiguated, finally, the text with entity pair is input into the relation discrimination model, and then the relation classification model is used to obtain the relation category of entity.The application can extract entity and relation in series for text data, and has the functions of long text processing, entity disambiguation and rich semantic function of anaphora resolution, and good entity extraction and relation extraction results are obtained.

The present application relates to a few-shot relation extraction method and device based on a multi-knowledge enhanced prototype network. The method includes: constructing a multi-knowledge enhanced prototype network composed of a semantic encoder, a multi-knowledge enhanced learning module, a dual contrastive learning module, and a relation prediction module. This network adopts prompt learning to design a prompt template with entity information to activate the knowledge in the pre-trained language model, and can obtain a more accurate instance semantic representation; at the same time, two prior knowledge of multi-granularity entity types and relation descriptions are introduced to enhance the semantic representations of instances and prototypes; and a dual contrastive learning module based on instances and prototypes is designed to learn the class distinctiveness and distinguishability of instance representations and prototype representations from two different levels of instances and prototypes, so as to be able to fully capture and understand the features of all relations in the text based on a small number of samples, and improve the accuracy of text entity recognition and relation classification prediction.

The application discloses a kind of textile relationship extraction methods based on deep learning, comprising the following steps: first, obtain the unstructured text data in textile field, pre-process text data to form data set, feature extraction is carried out to data set using neural network by relationship classifier, and reverse cross entropy is calculated;Second, the symmetric cross entropy is calculated using reverse cross entropy calculation, and is used as the loss function of relationship classifier;Third, two independent, same structure relationship classifiers are used, and respective loss function is used for training, respectively, after the loss of each is calculated, the total classification loss of both is calculated;Fourth, the total symmetric cross entropy between the prediction probability of two relationship classifiers is calculated, which is used as the joint loss of common regularization term and total classification loss, and the joint loss is used to train two relationship classifiers respectively.The method not only can reduce the influence of noise label, but also can improve the classification accuracy, and has good relationship extraction performance.

The present invention provides a multi-task event relationship extraction method and system based on relative time analysis, which relates to the field of natural language processing. By acquiring text data to be analyzed; inputting the text data into a pre-trained language model ERNIE, and combining the Masked Language Modeling task to enhance the pre-trained language model ERNIE's understanding of events and context semantics; constructing a multi-task learning framework based on relative time prediction; through the multi-task learning framework, jointly training event relationship classification tasks and event relative time prediction tasks, optimizing the model parameters of the pre-trained language model ERNIE; outputting the time relationship between the at least two events. Reduce the gap between the classification task and the pre-training task, thereby improving the model's effect on classifying relationships between events.

The application discloses an electromagnetic space field entity relationship joint extraction method with additional time information, comprising the following steps: obtaining social platform open source text data by using a Python crawler technology; performing text cleaning and preprocessing on the obtained open source text to form a data set C; determining entity and relationship classification, and formulating an entity relationship sequence joint labeling strategy; labeling the data set C with a triple tag; the triple tag is (main entity Subject, relationship Predicate, and object entity Object); an entity relationship joint extraction model STERM based on fragment sorting is constructed, and an entity relationship triple prediction model is obtained through training; and a (main entity, relationship, object entity, and time) quadruple list is obtained through a sequential priority nearest matching algorithm. The method solves the problems of error accumulation and exposure deviation, entity nesting and relationship overlap, and the problem that an entity relationship triple cannot represent dynamic changes of a relationship.

A relation extraction method for a knowledge graph automatic construction system. First, the text is encoded into word vectors to preliminarily extract text features. Then, the syntactic dependency structure of the text is used to generate a syntactic dependency tree, and a weighted dependency adjacency matrix is generated by weighting each relation category, and a graph convolutional neural network is used to extract the syntactic dependency information in the text. Synchronously, the multi-head attention mechanism is directly applied to the encoded text to generate an attention matrix, and a graph convolutional neural network with the same structure is used to extract information other than the syntactic dependency information of the text itself. Finally, the feature representations of two entities and the sentence itself are obtained, and a feed-forward neural network and a softmax function are used to score all possible relation categories, and the relation with the highest score is selected as the relation classification result. The present invention can fully obtain information in different dimensions of the text and has achieved excellent results on the public dataset for relation extraction.

Methods and apparatuses are described for generating a domain-specific knowledge graph from unstructured computer text. A computing device extracts unstructured computer text associated with pairs of entities from domain-independent documents, and trains an entity relationship classification model using a domain-independent knowledge graph and the extracted text. The computing device extracts other unstructured text associated with a first domain from domain-specific documents. The computing device identifies pairs of entities contained within the text for the first domain, and executes the trained model to determine a relationship between the entities in each pair of entities identified from the text for the first domain. The computing device generates a domain-specific knowledge graph using (i) the pairs of entities identified from the text for the first domain and (ii) the relationships between the entities in each pair of entities identified from the text for the first domain.

The invention relates to a knowledge graph completion system and method for image-text fusion and multi-task dynamic optimization, and the system comprises a data preprocessing module which is used for carrying out the preprocessing of entity relation text description and graph structure data; the data coding module is used for coding the preprocessed text and graph structure data and extracting features; the feature fusion module is used for dynamically fusing graph structure features and entity text semantic features; and the multi-task learning module is used for jointly optimizing link prediction, relation classification and path reasoning tasks based on a strategy and outputting a completion result. Dynamically fusing a graph structure and text semantic features through data preprocessing, coding, feature fusion and a multi-task learning process to realize knowledge graph completion; and in combination with a multi-task dynamic optimization strategy, the accuracy and efficiency of knowledge graph completion are effectively improved.

The application discloses a kind of multi-modal relationship extraction methods based on target-oriented description enhancement and dynamic weight, it is related to multi-modal natural language processing technical field.The method includes: obtaining original text, original image and corresponding target entity pair;Based on original text, target entity pair and preset directional guide prompt word template, target guiding description text is generated using large language model;Original image is input into target diffusion model, and semantic enhanced image is output;Original text, target guiding description text, original image and semantic enhanced image are input into pre-trained target relationship extraction network, and the relationship classification result for target entity pair is output.The application uses the target guiding description text constructed for target entity pair, can effectively drive target relationship extraction network to accurately extract the interactive features related to target entity pair in complex social media background, so as to improve the accuracy of multi-modal relationship extraction result and system robustness.