90 results about "Sequence labeling" patented technology

The invention provides a multi-mode command entity recognition method based on course comparative learning, and belongs to the technical field of natural language processing and computer vision. The method comprises the following steps: respectively carrying out semantic representation modeling on text and image data to obtain text features and image features; multi-level semantics from coarse granularity to fine granularity between the text features and the image features are aligned through staged comparative learning, and association between the text features and the image features is enhanced; fusing the text features and the image features through a gating multi-interest fusion mechanism to obtain fusion features; and performing sequence labeling on the fusion features by using a dynamically weighted conditional random field model. By combining a learnable gated multi-interest fusion module and a category-sensitive dynamic decoding mechanism, the problems of modal noise interference and category imbalance are effectively relieved, and the accuracy and robustness of multi-modal entity recognition and the applicability and stability of the model in a real social media environment are improved.

The invention discloses a personalized English learning recommendation method and system based on a knowledge graph, and relates to the technical field of intelligent education and natural language processing, and the method comprises the steps: collecting the multi-source learning data of a learner; extracting four types of knowledge entities including vocabularies, grammar, topics and skills by adopting a sequence labeling model, and calculating entity association degree through an attention mechanism to construct a hierarchical knowledge graph; constructing a user ability model based on the learner data, and positioning weak knowledge entities and associated entities thereof to form a target knowledge entity set; screening matched contents from a resource library, analyzing and determining a knowledge logic sequence in combination with a knowledge graph path, and generating a personalized recommendation result; and evaluating the effect according to the learned data and dynamically updating the user capability model and the knowledge graph. According to the invention, structured organization of English knowledge, accurate description of learner ability and coherent recommendation of learning content are realized, and the personalized level and effect of English learning are effectively improved.

The invention relates to the technical field of information retrieval and natural language processing, and particularly discloses a minimum evidence span alignment and accurate reference generation method and system. According to the method, candidate terms are obtained through direct numbering and semantic recall, Span-level evidence alignment is achieved through fusion of sequence labeling and interpretable attribution, the boundary robustness is optimized in combination with anti-fact boundary learning, closed-loop mending is triggered based on the minimum span coverage rate, and finally checkable reference with a standardized RefTag is output. According to the method, the problems of coarse evidence granularity, boundary drift and unreviewable reference of a traditional method are effectively solved, the generation illusion is remarkably reduced, and the auditing traceability is improved.

This invention discloses a method and system for labeling prompt words in a large-scale communication domain model based on knowledge graphs. Belonging to the fields of communication technology and artificial intelligence, it addresses the following technical problems: low efficiency in labeling specialized terms in communication domain prompt word annotation, low accuracy and efficiency of automatic recognition, limited prompt word dimensions, and lagging domain knowledge. The method includes: sequence labeling and entity extraction using a trained combined model to output entities; generating relation triples as candidate relations using a trained generator, and scoring and ranking the selected relation triples using a trained discriminator; semantic parsing of user input to extract key entities and intents; multi-hop reasoning from a dynamic knowledge graph to retrieve relevant entities and relations; eliminating illusory content using a trained GAN network; generating entity prompt words from three levels: basic, extended, and application dimensions; and introducing a triple quality verification mechanism.

The application relates to the field of natural language processing, and relates to a campus safety emergency extraction method based on multi-task learning, which comprises the following steps: S1: obtaining original news text of a campus safety emergency; S2: establishing a campus safety emergency extraction model, which comprises a text feature representation module, an event type classification module, a trigger word extraction module and an argument role classification module; S3: executing the text feature representation module to obtain a word vector fused with semantic dependency information; S4: executing the event type classification module to obtain a predicted event type feature vector; S5: executing the trigger word extraction module to obtain a trigger word feature vector; and S6: executing the argument role classification module to obtain an argument role. The method solves the problem that the sequence labeling method leads to labeling conflicts and cannot extract overlapping argument roles; and the method solves the problem that it is difficult to fully extract semantic knowledge and dependency relationships in a sequence in a professional field. The feature expression capability of trigger words is strengthened, and the argument role extraction capability is improved.

The embodiment of the invention relates to a model content auditing method and device, computer equipment and a storage medium, and the method comprises the steps: inputting an original text into a sequence labeling model, and outputting a risk fragment, a risk content confidence coefficient and an attack type in the original text; when the confidence coefficient is greater than a first threshold value, inputting the original text, the risk fragment and the attack type into a trained input auditing model, and outputting a first auditing result; when the first audit result is that the input content is compliant, generating output content; and inputting the output content, the risk fragment and the attack type into the trained output auditing model, so that the output auditing model audits the output content. Therefore, the risk fragments and the attack types are primarily screened through the sequence labeling model, the input content is audited through the input auditing model, the output content is audited through the output auditing model according to different application scenes, and the auditing efficiency, the jail break attack resistance, the accuracy and the safety are improved.

The invention discloses a novel power system standard self-adaptive generation method and system based on a knowledge graph, and belongs to the technical field of power grid data, and the method comprises the steps: carrying out the semantic enhancement and sequence labeling of a standard corpus through an entity extraction model, obtaining an entity, extracting an entity relationship in the standard corpus through a relationship extraction model, and obtaining an entity relationship; constructing an electric power knowledge graph; performing mapping matching on the scene query request and the electric power knowledge graph to obtain a to-be-queried sub-graph, and traversing the electric power knowledge graph by taking the to-be-queried sub-graph as a traversal reference and taking a scene node in the to-be-queried sub-graph as a traversal starting point to obtain a candidate standard set; and constructing a suitability evaluation function by taking the maximum coverage of the scene nodes and the maximum matching degree of parameters corresponding to the technical index nodes in the to-be-queried sub-atlas as targets, and scoring the candidate standard set through the suitability evaluation function to obtain a standard recommendation list. The technical problem that the standard adaptability of a novel power system is difficult to improve in the prior art is solved.

The invention discloses a synchronous phase modifier fault diagnosis method based on a knowledge graph and a large language model, and the method comprises the steps: obtaining the operation data of a synchronous phase modifier, and carrying out the preprocessing of the operation data, so as to form a unified corpus and a standardized data set; based on the unified corpus, using a BERT model to encode the corpus to obtain context perception vector representation, and based on the context perception vector representation, performing sequence labeling and relation extraction, and outputting structured knowledge data; based on the structured knowledge data, constructing a synchronous phase modifier fault diagnosis knowledge graph; based on a pre-trained large language model, performing model training and optimization by using the standardized data set, understanding and analyzing a fault phenomenon described by a natural language, and outputting a semantic analysis result; and in combination with the output of the knowledge graph and the semantic analysis result, performing hybrid reasoning to obtain a fault diagnosis result. The method has the advantages of high diagnosis precision, high diagnosis efficiency and the like.

The invention provides a training teaching video intelligent analysis and knowledge point automatic marking method based on multi-modal fusion, and the method comprises the steps: collecting multi-modal data in a training teaching process, and carrying out the time alignment processing; extracting feature information of the modal data, fusing the feature information through a cross-modal fusion architecture, and generating a unified teaching behavior representation vector; based on the teaching behavior representation vector, identifying operation steps in the practical teaching process through a sequence labeling model, and determining the category and the starting and ending time boundary of each operation step; matching the identified operation steps with a preset skill knowledge base, and generating a standardized knowledge point label containing knowledge point content, starting and ending timestamps and confidence information; and storing the standardized knowledge point labels into a database, and constructing a retrieval index. According to the invention, the unstructured teaching video is converted into a searchable, navigable and analyzable knowledge unit, and automatic identification and structured marking of practical teaching operation are realized.

This invention discloses a method for constructing a database for the survey of traditional Chinese medicine (TCM) cultural resources based on knowledge graphs, belonging to the field of database construction technology. Specifically, it includes: collecting classical texts and field survey texts; extracting core entity terms using sequence labeling technology; establishing an ontology model including lineage and medicinal material origin dimensions to form a knowledge architecture for the TCM field; extracting entities and events from the text using neural networks, aligning entities to ontology nodes according to the domain knowledge architecture, and generating a basic TCM resource graph; calculating feature vectors of graph nodes, using inference algorithms to mine implicit connections between origin changes and treatment customs, and completing missing semantic edges in the graph; performing multiple path searches for resource nodes, aggregating their associated nodes in the lineage and regional dimensions, and generating a subgraph containing spatiotemporal evolution trajectories; and converting the generated subgraph into topological data to complete the construction of a network survey database.

This application provides a method, apparatus, and electronic device for generating training data based on psychological process modeling. The method includes: acquiring a set of target text segments; inputting the set of target text segments into a trained binary classification model to obtain a classification result for each text segment in the set; selecting at least one target text segment from the set whose classification result indicates it contains cognitive linkage information; inputting the at least one target text segment into a trained sequence labeling model to obtain at least one linkage data group corresponding to each target text segment; inputting the at least one linkage data group and preset constraint rule information into a trained causal language model to obtain psychological link information corresponding to each linkage data group; and constructing training data based on each linkage data group and its corresponding psychological link information. This solution enables explicit modeling of the psychological motivations behind human interaction behavior, improving the quality of training data.

The application discloses a trigger word recognition and positioning system and method based on BIO sequence labeling, comprising: using an encoder to extract a whole sentence semantic vector, judging whether the input text contains a backdoor attack feature; if the backdoor suspicion is detected, positioning the backdoor trigger word, judging whether each word belongs to the backdoor trigger word through the BIO sequence labeling of each token, and outputting a preliminary trigger word position marking sequence; applying trigger position prior knowledge and multi-strategy rules to correct and optimize the results and filter false positives; when the results have uncertainty or are suspected to have attack avoidance, using a predefined known trigger phrase mode library to scan the input text, capturing hidden or variant trigger word modes, and positioning the missed suspicious trigger words; and outputting the final result of backdoor detection. Through the dual-module cooperation on the architecture and the priori and rule fusion on the strategy, the application can robustly detect the text backdoor trigger and accurately position the trigger content.

The application relates to a video segmentation method, a recommendation method, a system, a device and a medium. The segmentation method comprises the following steps: obtaining a target video; performing voice recognition on the target video to obtain a plurality of subtitle sentences corresponding to the target video and starting and ending moments of the subtitle sentences in the target video; calculating time intervals between each subtitle sentence and a previous subtitle sentence according to the starting and ending moments of each subtitle sentence in the target video, and fusing each subtitle sentence and the corresponding time interval to obtain a plurality of fusion sentences arranged in sequence; identifying and extracting each chapter name from the plurality of fusion sentences based on a sequence labeling algorithm, and determining a corresponding chapter starting moment; and segmenting the target video into a plurality of chapter videos according to each chapter name and the corresponding chapter starting moment. The application improves the accuracy of video segmentation.

The application provides an event information extraction method and device and electronic equipment, and relates to the technical field of natural language processing. The method comprises the following steps: obtaining a sentence to be processed; inputting the sentence into a sequence labeling model to obtain a vector corresponding to the sentence, an entity in the sentence, and a trigger word; for each entity-trigger word pair, concatenating the vector corresponding to the sentence, a position vector of the trigger word in the sentence, and a position vector of the entity in the sentence to obtain a concatenated vector; and inputting a plurality of concatenated vectors into a text classification model to obtain event information in the sentence. Thus, the sequence labeling model and the text classification model are used to automatically extract event information in the sentence, thereby reducing the labor cost, and the event information can be accurately extracted for sentences in different fields and scenarios, and the portability is high.

The invention relates to a text classification method fusing semantic information and structural information, and aims to solve the problems of text graph noise interference, incomplete semantic capture, rigid information fusion and the like in existing graph neural network text classification. The method comprises the steps that firstly, a target text is preprocessed, and phrase blocks with complete semantics are extracted through BERT sequence labeling and B-I-O labeling; constructing an enhanced text graph by taking the phrase blocks as nodes and combining various relationships such as self-loop edges and syntactic dependency edges and cross-sentence anaphora connection; afterwards, redundant edges in the picture are cut through attribute-enhanced personalized PPR, and noise interference is weakened; and finally, inputting the optimized text graph into gradient gating fusion GNN, adaptively integrating BERT context features and global dependency information, outputting node features and completing classification. The method effectively breaks through the limitation of a traditional method, realizes deep fusion of semantic and structural information, has higher classification accuracy, stronger generalization ability and good stability, and is suitable for various short text, long text and professional field text classification scenes.

The invention discloses a matcha industry innovation and entrepreneurship project tracking system, which belongs to the technical field of project tracking and comprises a project construction drawing design module, a project construction drawing decomposition module, a project construction drawing sequence labeling module, a project construction material drawing coordinate conversion module and a project construction progress planning distribution module. The system comprises a project construction information acquisition module, a project construction material sequence marking module, a project construction material image coordinate conversion module, a project construction progress tracking module and a project construction progress sharing module. The whole process is collected through equipment, tampering cannot be carried out manually, the problem of progress misjudgment is avoided, and meanwhile, videos are collected in real time, and responsibility tracing is facilitated.

The invention discloses a trigger word recognition and positioning system and method based on BIO sequence tagging, and the method comprises the steps: extracting a whole sentence semantic vector through an encoder, and judging whether an input text contains backdoor attack features or not; if backdoor suspicion is detected, backdoor trigger word positioning is carried out, whether each word belongs to a backdoor trigger word or not is judged through token-by-token BIO sequence labeling, and a preliminary trigger word position labeling sequence is output; performing result correction and optimization by applying trigger position priori knowledge and a multi-strategy rule, and filtering false alarms; when the result is uncertain or suspected to have attack avoidance, scanning the input text by using a predefined known trigger phrase pattern library, capturing a hidden or variant trigger word pattern, and positioning missed suspicious trigger words; and outputting the final fruit of the back door detection. According to the method, through dual-module cooperation on architecture and priori and rule fusion on strategy, text backdoor triggering can be detected in a robust mode, and triggering content can be accurately positioned.

Embodiments of the present disclosure provide a method for determining a motion sequence model based on a body robot, a method for determining a motion sequence and related products, which relate to the technical field of body robots, and the method comprises: obtaining training samples and motion sequence labels; processing a plurality of image frame pairs and task instructions using a motion sequence model with frozen parameters to obtain visual text feature information and key frame weights, and determining reference state feature information based on the key frame weights and state frames; processing the visual text feature information and the reference state feature information using a to-be-trained adaptation module to obtain a reference motion sequence; in the training process of the motion sequence model, adjusting the parameters of the to-be-trained adaptation module based on a loss function value determined based on the motion sequence labels, the reference motion sequence and the key frame weights, and keeping the backbone parameters of the motion sequence model unchanged until the trained motion sequence model meets a training stop condition. In this way, the prediction accuracy of the motion sequence is improved.

The invention provides an audio-visual large model user file updating method and system. The method comprises the following steps: acquiring an audio-video stream in real time; extracting a plurality of face feature vectors and voiceprint feature vectors corresponding to the face feature vectors based on the audio and video streams; updating a pre-stored user file based on the face feature vector and the voiceprint feature vector to obtain a first user file; based on the audio and video streams and a preset sequence labeling model, obtaining effective dialogue data, and converting the effective dialogue data into text data; and updating the first user file based on the text data and the audio and video stream to obtain a second user file so as to update the user file. According to the audio-visual large model user file updating method and system provided by the invention, the pre-stored user file is updated through the face feature vector and the voiceprint feature vector, so that the user information is supplemented and perfected, the accuracy and timeliness of the user file data are effectively improved, and the user experience is improved. And the situation that the user file cannot be updated due to limitation to a text mode is avoided.

The embodiment of the invention provides an action sequence model determination method based on a body robot, an action sequence determination method and a related product, and relates to the technical field of body robots, and the method comprises the steps: obtaining a training sample and an action sequence label; processing the plurality of image frame pairs and the task instruction by using a parameter frozen action sequence model to obtain visual text feature information and a key frame weight, and determining reference state feature information based on the key frame weight and the state frame; processing the visual text feature information and the reference state feature information by using a to-be-trained adaptation module to obtain a reference action sequence; and in the training process of the action sequence model, parameters of the to-be-trained adaptive module are adjusted based on a loss function value determined by the action sequence label, the reference action sequence and the key frame weight, and trunk parameters of the action sequence model are kept unchanged until the trained action sequence model meets a training stop condition. Therefore, the prediction precision of the action sequence is improved.

The application provides an audio segmentation method and device, electronic equipment and storage medium, wherein the method comprises: obtaining audio to be segmented; extracting acoustic features of each frame in the audio to be segmented, and based on the acoustic features of each frame, performing semantic boundary sequence labeling on the audio to be segmented to obtain semantic boundary labeling results of each frame; and based on the semantic boundary labeling results of each frame, performing segmentation on the audio to be segmented. The method, device, electronic equipment and storage medium provided by the application can assist semantic segmentation based on tone and pause information in the acoustic features of each frame, retain complete semantic information of the audio, and avoid punctuation recognition errors, thereby improving the accuracy and reliability of audio segmentation. Furthermore, the method can be applied to a cascaded speech translation system and an end-to-end speech translation system, thereby expanding the application range of audio segmentation.

The invention relates to a TSN configuration method based on a natural language processing technology, and belongs to the technical field of natural language processing and network communication. A user inputs a natural language configuration instruction through the user plane APP, and the natural language configuration instruction is analyzed by the NLP module to generate structured data and sent to the CUC; and forwarding the data to the CNC or TSN configuration system, and completing switch configuration by the TSN configuration system or the CNC. According to the method, text features are extracted through BERT, time sequence dependence is enhanced through LSTM, sequence labeling is optimized through CRF, structured data are generated through a JSON formatting module, and precise conversion from a natural language to a configuration instruction is achieved. An NLP automatic analysis process improves the configuration efficiency; the BERT + LSTM + CRF collaborative architecture enhances instruction analysis, and captures complex semantics and time sequence dependence; and in combination with CUC / CNC multi-path forwarding and standardized protocol conversion, the flexibility and compatibility of the system are enhanced, and the method is suitable for TSN efficient configuration under a dynamic network.

This application provides a method, apparatus, and electronic device for generating training data based on psychological process modeling. The method includes: acquiring a set of target text segments; inputting the set of target text segments into a trained binary classification model to obtain a classification result for each text segment in the set; selecting at least one target text segment from the set whose classification result indicates it contains cognitive linkage information; inputting the at least one target text segment into a trained sequence labeling model to obtain at least one linkage data group corresponding to each target text segment; inputting the at least one linkage data group and preset constraint rule information into a trained causal language model to obtain psychological link information corresponding to each linkage data group; and constructing training data based on each linkage data group and its corresponding psychological link information. This solution enables explicit modeling of the psychological motivations behind human interaction behavior, improving the quality of training data.

The application provides a sequence labeling method and a training method, a device, equipment and a medium, and the sequence labeling optimization method comprises the following steps: processing natural language based on a deep learning model, and assigning a sequence label to each word unit; wherein the word unit comprises at least one character or at least one word; determining whether the sequence label conforms to a sequence label rule based on a preset rule; if the sequence label does not conform to the sequence label rule, the sequence label is sent to a linear chain conditional random field (CRF) layer for correction; and the sequence label to be inferred by the model is pre-judged whether to be input into the linear chain conditional random field (CRF) layer, so that the calculation number of the CRF layer can be greatly reduced, and the calculation accuracy is ensured.

The invention discloses a colored lamp culture field named entity recognition method, which belongs to the technical field of natural language processing, and comprises the following steps: acquiring colored lamp culture text data, and extracting context feature representation through a pre-training language model; according to the context feature representation, applying adversarial disturbance in an embedding space to obtain a feature representation with optimal robustness; according to the feature representation with the optimal robustness, deep semantic coding is carried out through a residual bidirectional long-short-term memory network, and coding features are obtained; according to the coding features, sequence labeling decoding is carried out through a conditional random field, and predicted entity fragments are obtained; according to the predicted entity segment, decoding and filtering after type sensing are carried out, and a final named entity recognition result is obtained. According to the method, accurate identification of high-density process terms and heterogeneous data in the colored lamp culture field is realized, and high-quality entity extraction capability is provided for intangible cultural heritage digitization and knowledge graph construction.

The application discloses a power industry entity relation extraction method, device, equipment and medium, and the method comprises the following steps: acquiring a power industry text dataset and preprocessing, based on a power industry entity query template, extracting power industry entities by using a generative pre-training language model, and constructing a labeled entity dataset; the context features of the power industry text dataset are extracted and sequence labeling is performed by using an ELMO model and a Transformer-CRF model, and a global dependency relationship is obtained by modeling; based on the global dependency relationship, the context features of the text data to be labeled are extracted and sequence labeling is performed, an entity label sequence is obtained, the continuous and same entity labels in the entity label sequence are merged, and a plurality of entities are determined; based on the entity relation query template, the relation information is extracted by using the generative pre-training language model, and the relation instances are constructed according to the preset matching rule. The application can improve the accuracy and coverage of power industry entity relation extraction.

This invention belongs to the field of natural language processing and engineering cost estimation technology, specifically involving an automatic process segmentation method for engineering bills of quantities that integrates semantic understanding and sequence labeling. It achieves automatic bill of quantities segmentation by deeply integrating engineering domain knowledge and deep learning. Through atomized process segmentation and BIOA sequence labeling, process-level granularity is achieved, accurately separating different processes and providing a favorable premise for subsequent cost item matching. By introducing the BERT+CRF sequence labeling model, the semantic logic of construction is understood, and through the B-I tagging mechanism, the main process and its supplementary descriptions are automatically merged, maintaining the semantic integrity of the process. This overcomes the dual defects of existing technologies, namely, "illusion in overall prediction" and "unintelligent mechanical segmentation."

The application relates to the technical field of data processing, and discloses a customs document text target feature extraction method and system based on NLP. The method comprises the following steps: performing sequence labeling on a customs document text to obtain an initial field set and a field missing vector; matching the field missing vector with a structure type feature vector to determine a field space attention template; screening candidate text blocks according to the attention template to generate a completed field; and constructing a semantic association graph by combining the initial field with the completed field, and then performing constraint verification and iterative correction to output a target field set. The application improves the field extraction accuracy and logical consistency of customs documents with varying formats and complex formats.

The application discloses a kind of based on causal event extraction model's causal event extraction method, comprising the following steps: based on event extraction and causal relationship identification two subtasks, construct causal event extraction model;Acquire input sentence;Based on pre-training language model, input sentence is encoded, based on sequence labeling decoder, the coded input sentence is decoded, and the event of existence causal correlation is extracted;Based on causal event extraction model, construct initial background graph;Event is inserted into initial background graph, and updated background graph is obtained;Using graph neural network, the representation of extracted event is obtained by encoding and updating to updated background graph;Based on classifier and the representation of extracted event, the causal relationship between events is obtained, realizes the extraction of causal event.The experimental results of the sentence of different causal event pairs prove the ability of the method of the application to extract complex causal relationship.

The application relates to a Chinese medical entity recognition method and system based on a multi-granularity capsule neural network, which utilizes a pre-trained language model to obtain a character-level context semantic vector; the context semantic vector is sequentially input into a multi-head self-attention layer and a bidirectional gated recurrent unit layer to obtain time-series enhanced features; multi-granularity feature extraction and splicing are performed on the time-series enhanced features; a hierarchical entity structure model is established for multi-granularity fusion features by using a capsule neural network to obtain broadcast post-expansion digital capsule features; boundary enhancement features are obtained through boundary perception enhancement; the time-series enhanced features, the boundary enhancement features and the broadcast post-expansion digital capsule features obtained in step S4 are spliced, and the spliced result is output as a final entity label sequence through a sequence labeling decoding layer. Advantages: the method overcomes the defects of the existing Chinese medical entity recognition technology, such as insufficient modeling capability for complex structure entities, long entities and nested entities, and low entity boundary division precision.