125 results about "Sequence labeling" patented technology

The invention discloses an unregistered geographical name discovery and spatial position reasoning method and a related device, and relates to the technical field of geographic information, and the method comprises the steps: constructing a geographical name space-time derivation relation knowledge graph based on an open source geographic database; inputting the target place name text data into a sequence labeling model to obtain a labeling result, and extracting candidate derived place names based on the labeling result; performing general name segmentation on the candidate derived geographical names to obtain potential basic geographical names, and judging whether the potential basic geographical names meet preset association conditions or not based on a geographical name space-time derivation relationship knowledge graph; if yes, eliminating potential basic place names; if not, determining the potential basic place name as an unlogged place name; and determining the spatial position of the unregistered place name according to the general name of the unregistered place name and the place name space-time derivation relationship knowledge graph. According to the invention, the automation degree of unregistered place name discovery and spatial position reasoning can be improved.

The invention relates to the technical field of natural language processing, in particular to a foreign trade letter intelligent semantic error correction and business term optimization method and device, and the method comprises the steps: obtaining a target foreign trade letter, and constructing a target corpus; performing Chinese word segmentation and part-of-speech tagging on the target foreign trade letter; performing term optimization based on the word segmentation result and the knowledge graph; identifying the letter title by using a conditional random field model, and converting the letter title into structured data; a Bi-LSTM-CRF model is adopted to carry out risk point detection, including Bi-LSTM coding, feature engineering and Max-pooling technologies, a part-of-speech sequence is obtained through a softmax function and a Viterbi path, and sequence labeling is carried out to obtain a risk point detection result; and finally, performing Chinese error correction based on the word segmentation result after part-of-speech tagging and the knowledge graph. The recognition and correction accuracy of foreign trade terminologies is improved, and communication obstacles caused by nonstandard use of the terminologies are effectively reduced.

The application discloses a multi-intent spoken language understanding method based on syntax analysis, which comprises the following steps: firstly, obtaining an intent feature matrix and a slot feature matrix according to a user input sentence, and constructing a multi-level intent feature from the intent feature matrix; secondly, obtaining initial intent labels and initial slot prediction labels by using an intent decoding module and a slot decoding module respectively on the intent feature matrix and the slot feature matrix; then, inputting the multi-level intent feature and the initial slot prediction labels into a slot-intent interaction module to obtain an enhanced intent feature matrix, and inputting the slot feature matrix, the multi-level intent feature and the initial intent labels into an intent-slot interaction module to obtain an enhanced slot feature matrix; finally, inputting the enhanced intent feature matrix and the slot feature matrix into the intent decoding module and the slot decoding module respectively to obtain intent labels and slot sequence labels of the spoken language understanding task. The application improves the accuracy of intent recognition and slot sequence labeling and the accuracy of multi-intent recognition.

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 provides a micro-learning service information extraction method based on a deep sequence labeling model, in the micro-learning service information extraction process, a deep sequence labeling model is used: first, an embedding layer is used to map high-dimensional sparse original data into low-dimensional continuous but dense data representation, and semantic information is extracted; after the embedding layer, two continuous CNN layers are used to further mine and summarize the local features of adjacent inputs in the model; an RNN layer is further arranged after the embedding layer, which is used for extracting and modeling time sequence features; a fusion block is arranged after the CNN and the RNN, which is used for combining different types of potential features; a CRF layer is arranged before a final output layer, which is used for the local constraint of an output sequence.

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 radar confrontation domain knowledge extraction method and system based on a BERT model. The method comprises the following steps: firstly, establishing a dynamic and static combined radar confrontation knowledge unified description architecture; performing unstructured data preprocessing on radar confrontation text data from different data sources; then performing entity recognition and relation extraction by using a sequence labeling method based on a pre-training language model; and finally, based on a radar confrontation knowledge unified description architecture, performing knowledge fusion and knowledge representation on knowledge data extracted from different data sources to form a radar confrontation unified description knowledge set. The system is used for realizing the radar confrontation domain knowledge extraction method based on the BERT model. According to the method, knowledge extraction and unified description can be carried out on unstructured text data in the radar countermeasure field, and the method has the advantages of high specialty, high accuracy and high extraction efficiency.

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 two-channel electric power entity recognition method and system based on BIBASRU and improved Reformer, and relates to the technical field of natural language processing and electric power information extraction, and the method comprises the steps: employing a MacBERT model to carry out pre-training semantic coding, and extracting context semantic features; constructing a BIBASRU structure as a first channel; a Reformer model based on ROPE optimization is constructed to serve as a second channel, and learnable position embedding is replaced with an ROPE coding mechanism; designing a dual-channel fusion mechanism, and dynamically weighting and integrating two paths of feature outputs; and finally, introducing a CRF layer to carry out sequence labeling decoding. According to the method, high-precision and high-efficiency entity recognition is realized in professional texts such as electric power regulations, scheduling logs and equipment reports, the method is remarkably superior to traditional BERT-BiLSTM-CRF models and the like, and the method is suitable for intelligent power grid knowledge graph construction and automatic information extraction scenes.

The invention provides a named entity recognition method for a high-speed railway technical improvement and overhaul project text, which comprises the following steps of: preprocessing unstructured data and semi-structured data in a picture and / or a text, and converting character-level positions of entity labels by adopting BIOES (Basic Input / Output Element Specification) labeling to obtain a technical improvement and overhaul data set; performing data enhancement on the technical improvement and overhaul data set by adopting a data enhancement strategy to obtain a data enhancement result; and inputting the text data subjected to data enhancement into an RBC fusion model, converting the text data into character vectors, carrying out a sequence labeling task to obtain a prediction label result of each character, and finally realizing an entity of a technical renovation and overhaul text through a large model prompt project method. Through preprocessing, data enhancement and an RBC fusion model, non-structured and semi-structured data in the high-speed rail technical improvement and overhaul project are efficiently identified, the problem that key information cannot be dynamically extracted through a traditional method is solved, and the method has the advantage that the information extraction efficiency and accuracy of the high-speed rail technical improvement and overhaul project are improved.

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.

This invention proposes a chat topic recognition method and system based on text tag prediction. The method includes the following steps: acquiring and preprocessing the chat data information to be processed; processing the preprocessed chat data information using a trained deep learning model, including sentence encoding and sequence labeling; and identifying specific content from the chat data information using the deep learning model and further processing it. By introducing Sentence Bert to extract semantic features from chat statements, and using sentences as semantic units input into the model, the length limit of the feature labeling network is greatly expanded. The feature labeling method is used to label behaviors commonly seen in online fraud, ensuring that even if the wording is updated, the truly crucial inducement behaviors remain largely unchanged. By modifying the convolutional neural network model, the model can perform sequence labeling tasks while retaining the high inference performance of the convolutional neural network.

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 a method and device for extracting data from a structured document. The method comprises obtaining a text data set of the structured document, determining sequence labeling data of the text data, determining a first data field type of the text data based on the sequence labeling data, determining a second data field type of the text data, and extracting text data corresponding to a preset data field type from the text data set based on the first field type and the second field type. By using the above method and device, valuable information can be quickly, accurately and intelligently parsed and extracted from the structured document.

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.