1214 results about "Target text" patented technology

The invention relates to the technical field of machine vision, and discloses a machine vision-oriented cross-modal image-text analysis method, which comprises the following steps of: partitioning an input image to generate an image block sequence; inputting the image block sequence into a visual converter for multi-scale feature extraction, and generating target visual features; encoding the input text to generate a target text feature; inputting the target visual features and the target text features into a deep reconstruction bottleneck network for compression alignment, and generating a cross-modal compression vector; and inputting the cross-modal compression vector into a large language model to generate cross-modal decoding information, so that cross-modal redundant information can be effectively filtered, compact shared semantic representation can be learned, the information integrity of the compression process is ensured through bidirectional reconstruction verification, cross-modal semantic alignment is realized, and the method has the advantages of high efficiency and high reliability. Omnibearing cross-modal content generation from the whole to details is achieved, and the requirements of different application scenes are met.

The invention relates to the technical field of electrical digital data processing, and discloses a system conflict point intelligent review system based on a knowledge graph and a large language model, which comprises the following steps: constructing a dual-mode storage space containing an unstructured index and a structured logic graph, analyzing target text extraction features and triggering graph-based generation logic; converting the topological structure of the associated sub-atlas into a natural language instruction sequence to construct a forced logic constraint template, filling the template with a text, and inputting a pre-training language model to generate a verification result; according to the method, the discrete atlas topology is mapped into the linear logic constraint, random divergence of the generative model is restrained on the calculation principle, and precise decoupling and dynamic evolution of unstructured semantics and structured logic are achieved.

The invention relates to the technical field of voice processing, and provides a rhythm migration method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a decoupled rhythm feature based on a source rhythm voice, and a decoupled tone feature based on the voice of a target speaker, the decoupled rhythm feature represents the rhythm of the source rhythm voice, and the decoupled tone feature represents the tone of the target speaker; the decoupled timbre features represent the timbre of the voice of the target speaker; generating a target voice vector sequence based on the text features of the target text, the voice features of the voice of the target speaker, the decoupled rhythm features and the decoupled timbre features; and synthesizing a target audio based on the target voice vector sequence. According to the method and the device, the decoupled rhythm features and the decoupled timbre features are acquired, and the target voice is generated based on the features, so that the problem of feature mixing is effectively relieved, the timbre purity of the target speaker in cross-person rhythm migration is ensured, the expressive force of rhythm migration is improved, and the synthesized audio is more natural and vivid.

The invention provides a text segmentation method and related equipment. The method comprises the steps of obtaining a to-be-processed text; segmenting the to-be-processed text into ordered statement sequences to obtain an initial statement set of the to-be-processed text; wherein the ordered statement sequence comprises a plurality of statements; the semantic variation, the confusion degree variation and the information entropy variation of a first target text block are calculated when a to-be-decided statement in an initial statement set of the to-be-processed text is added into the first target text block, and the first target text block is a set of multiple statements meeting a merging condition; determining the collaboration degree of the statement to be decided and the first target text block based on the semantic variable quantity, the confusion variable quantity and the information entropy variable quantity; and partitioning the to-be-processed text based on the collaboration degree of the to-be-decided statement and the first target text block to obtain a target partitioned text of the to-be-processed text. The text segmentation quality can be improved.

The invention belongs to the technical field of large language models, and discloses a time sequence card generation method and device based on a large language model, and the method comprises the steps: receiving a card creation instruction, and obtaining a card data type in the card creation instruction; extracting target text data in a dialog box of the large language model based on the card data type; converting the target text data into target card data corresponding to the card data type; generating a card title corresponding to the target card data according to the target text data; combining the target card data and the corresponding card titles to obtain time sequence cards; and putting the time sequence card into a tool window adjacent to the dialog box for displaying. According to the method, the manual operation of the user can be reduced, and the co-creation work efficiency and convenience of the large language model and the user are improved.

The invention provides a trauma knowledge graph construction method and system based on a three-level fault-tolerant mechanism. The method comprises the following steps: acquiring a trauma condition core knowledge framework; extracting trauma condition rules based on the trauma condition core knowledge framework, and establishing a trauma condition rule base; dynamically generating a target cue word based on the trauma condition rule base by utilizing a large language model; processing the target text based on the target cue word by using a large language model to generate a candidate file; verifying the candidate file to obtain a verification file; and constructing the trauma knowledge graph based on the verification file. According to the scheme, a mode of combining ontology construction from bottom to top and entity node construction from top to bottom is adopted, various methods such as manual construction, rule extraction and a large language model are fused, the knowledge extraction precision and the map coverage degree are improved, the information processing capacity and the treatment decision-making efficiency in a complex trauma condition scene are remarkably improved, and the method is suitable for popularization and application. And construction of an intelligent medical system is supported.

The application relates to the technical field of text label marking, and provides a text specification marking method and system based on a rule corpus, which comprises the following steps: analyzing a policy and regulation document, identifying and marking condition morphemes and conclusion morphemes in the policy and regulation document, constructing a logical relationship between the two by using a large language model, and forming a rule corpus composed of structured morpheme pairs; performing semantic embedding on the corpus to generate a semantic vector library; performing multi-label coding on a verification data set based on the rule corpus, and constructing a multi-label training data set; training a deep learning classification model by taking semantic vectors as features and multi-labels as targets, so that a text specification marking model is obtained; and automatically marking target text by using the model. The application significantly improves the accuracy, interpretability and business adaptability of text marking, improves the update quality of a system label data set, and reduces the system maintenance cost.

The invention provides a small and medium-sized enterprise digital transformation intelligent question answering method and device, equipment and a storage medium, and the method comprises the steps: carrying out the semantic recognition and entity extraction of a question input by a user, and generating a question semantic vector; retrieving a corresponding target text fragment in a preset vector database according to the question semantic vector; searching a sub-graph structure associated with the question semantic vector in a preset knowledge graph according to the target entity of the question semantic vector; reordering the text segments and the sub-graph structures based on correlation scores to obtain an ordering result set; and inputting the sorting result set into a retrieval enhancement generation model, and generating question and answer output content. Through the implementation of the scheme of the application, association matching of question semantics and enterprise knowledge can be realized by utilizing the text semantic information and the knowledge graph structure information at the same time in the question and answer generation process, the consistency of question and answer contents in logic structure and semantics is ensured, and the accuracy of digital transformation question and answer of small and medium-sized enterprises is improved.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice generation method, device and equipment based on pseudo-autoregression modeling and a medium, and the method comprises the steps: obtaining a training sample containing a text sequence, a prompt voice segment and a target semantic token sequence; performing continuous fragment mask training on the text-to-semantic model to obtain a pseudo-autoregression trained text-to-semantic model; generating candidate speech output by using the text-to-semantic model and the initial semantic-to-acoustic model which are subjected to pseudo-autoregression training, and constructing a preference data pair; updating the semantics-to-acoustics model based on the preference data pair to obtain a preference optimized semantics-to-acoustics model; and generating target voice output based on the target text and the target prompt voice. According to the method, the time sequence modeling capability of the model is enhanced through pseudo-autoregression training, and the voice generation quality is directly optimized through the preference data pair, so that the voice alignment precision and the subjective listening feeling performance are improved.

This invention relates to the field of text processing technology, and in particular to a method, device, and medium for processing navigational notice text based on semantic enhancement. The method includes: first, acquiring a content carrier to be processed; then, acquiring the semantic vector and glyph feature vector of the content carrier; concatenating the two types of vectors to form an enhanced text representation; extracting temporal features from the enhanced text representation to obtain temporal features containing forward and backward logical relationships within the text; acquiring the weights of words and sentences in the temporal features and performing weighting to obtain weighted word representations and weighted sentence representations; performing correction processing on the weighted representations to generate corrected text; and finally, validating the corrected text and outputting the target text. This invention can improve the accuracy and efficiency of content carrier processing.

An audio synthesis method, an audio synthesis model training method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product, which relate to artificial intelligence technology. The method includes: invoking an audio synthesis model based on language information and preset style information of a target text to perform following processing, the audio synthesis model including a prior encoder and a waveform decoder: generating audio features corresponding to the target text based on the language information and the preset style information by using the prior encoder; performing normalizing flow processing on the audio features by using the prior encoder, to obtain a hidden variable of the target text; and performing waveform decoding on the hidden variable of the target text by using the waveform decoder, to obtain a synthetic waveform conforming to an audio style described in the preset style information and corresponding to the target text.

An end-cloud cooperative data mining method, comprising: determining a target text and a task configuration file according to a business requirement by a cloud end; encoding the target text by a text encoder to obtain a text feature; placing the text feature in the task configuration file and issuing it to a vehicle end together; encoding image data by a first picture encoder to obtain an image feature by the vehicle end; calculating a value of a similarity of the text feature and the image feature; determining a target picture according to the value of the similarity and the task configuration file; and uploading the target picture to the cloud end; wherein the first picture encoder is obtained by compressing and optimizing a second picture encoder; and the text encoder and the second picture encoder are two modules of a picture-text multimodal large model. The application is applied to an automatic driving shadow mode, can complete data mining of any interesting target text by using one large model, and does not need to design and develop detection rules for each type of interesting target. The picture encoder of the vehicle end can be continuously updated and optimized, and the model iteration efficiency is improved.

The present disclosure relates to the technical field of multimodal learning, and particularly relates to a multimodal interleaved image-text generative model based on a dynamic feature synchronizer. An image encoder extracts multi-resolution multi-scale feature maps from input images of interleaved image-text data; and a dynamic feature synchronizer in a multimodal large language model acquires fine-grained information, so as to determine output feature data corresponding to the interleaved image-text data and then generate a target image and / or target text associated with the interleaved image-text data.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an identification method, device and equipment based on semantic space consistency, and a medium, and the method comprises the steps: obtaining and preprocessing a target image, a positioning text region and an object region, and generating region positioning information; identifying and correcting the text region to generate a target text vector, extracting an object region image feature and normalizing to generate a target image vector, and mapping the two to the same semantic space for consistency analysis to generate a fusion confidence coefficient and a preliminary identification result; and retrieving, comparing and outputting final identification information and the fusion confidence coefficient in a preset knowledge base based on the fusion confidence coefficient and the preliminary identification result. According to the method, complementary fusion of the text and the image is realized through semantic space mapping, and standardized verification is completed by combining with knowledge base comparison, so that the problem that a single recognition mode is easy to make mistakes is effectively solved, and the accuracy and reliability of a result are improved.

The invention discloses a data query method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: determining domain information corresponding to a to-be-processed document; determining a preset cleaning rule base corresponding to the field information, and identifying and correcting an error text in the to-be-processed document according to the preset cleaning rule base; segmenting the to-be-processed document after the error text is corrected to obtain a plurality of text blocks, and storing the text blocks and text block vectors corresponding to the text blocks into a database; and after a query instruction sent by a target object is received, determining a target text block corresponding to the query instruction according to the query vector and the text block vector corresponding to the query instruction, and generating a query result according to the target text block. The technical problem that the documents cannot be effectively corrected due to the fact that the same set of universal cleaning rules is adopted for all the documents in the prior art is solved.

The invention provides an embedded data synthesis method and device fusing retrieval and large model distillation and a medium. The method comprises the following steps of: preprocessing an unstructured document in a vertical field, and dividing the unstructured document into multi-granularity text blocks with a hierarchical association relationship; forming a context based on the combination of the multi-granularity text blocks, injecting disturbance information corresponding to the priori knowledge in the vertical field into the context, calling a generative model to generate a retrieval query according to the context, and determining a target text block corresponding to the retrieval query as an initial positive sample; false negative sample text blocks are filtered according to the incidence relation between the text blocks, and a positive sample set and a negative sample set are formed; and constructing a comparative learning training sample, and training the semantic representation model by using the comparative learning training sample to generate an embedded vector for the retrieval task. According to the method, the retrieval task construction efficiency and authenticity can be improved, the positive sample coverage integrity is improved, and the contrast learning training stability and retrieval precision are enhanced.

The embodiment of the invention provides a text generation method and device, a model training method and device and computing equipment. The text generation method comprises the steps of obtaining to-be-processed data; the to-be-processed data is input into a content generation model, initial probability distribution of all lexical elements of the target text is obtained, and the initial probability distribution is used for indicating the initial probability that the lexical elements are candidate lexical elements; obtaining a target feature vector of the target text according to the initial probability distribution and the embedding vector of each candidate lexical element; the target feature vector is input into a content generation model, updating probability distribution of all lexical elements of the target text is obtained, the target text is generated based on the updating probability distribution, and the updating probability distribution is used for indicating the updating probability that the lexical elements are candidate lexical elements. And a parallel decoding mechanism and a global feature optimization mechanism greatly improve the generation efficiency while ensuring the accuracy and continuity of the generated text.

The invention discloses a multi-agent-based text verification method, and relates to the technical field of agents, and the method comprises the steps: carrying out the core pain point recognition of a target text through a logic verification agent; constructing a logic chain entry used for outputting a logic verification result; judging whether the logic chain entry relates to external knowledge dependence or contains quantifiable parameters based on a scheduler, and scheduling a fact verification agent to execute a fact verification task, and / or scheduling a numerical value verification agent to execute a numerical value verification task; the scheduler carries out decision on each logical chain entry in combination with the evidence weight, the semantic matching degree and the consistency constraint to generate a decision conclusion; the memory memory encodes all results and corresponding original text positioning information into vectors and stores the vectors in a shared memory bank; and generating a verification report, and providing specific revision suggestions and original text positioning information. According to the method, logic verification, fact verification and numerical verification can be performed on the text, and the text verification efficiency and quality are improved.

The invention discloses a language model detection method and device. The method comprises the steps that a target text is obtained, and the target text comprises abnormal content expected to be output by a to-be-tested model; a pre-trained proxy model is adopted to analyze the target text, prompt words are generated, and model parameters of the proxy model are obtained by updating according to a reward value corresponding to an output result of the proxy model and a near-end strategy optimization algorithm; and inputting the cue word into the to-be-tested model, and detecting the content output by the to-be-tested model to obtain a detection result. The method provided by the invention at least solves the problem that the matching accuracy with the to-be-tested model is low due to the fact that the induction text is generated by adopting the manually-labeled template cue word in the related technology.

A system and method of determining a sentiment of a participant in an interaction may include: obtaining a plurality of textual segments, each representing a portion of the interaction, and labeled according to a specific participant; inferring a language model on one or more textual segments of the plurality of textual segments, to generate respective semantic embedding vectors, each representing a semantic meaning of the respective textual segment in a semantic vector space; compiling a semantic vector set that includes (i) a target semantic embedding vector, corresponding to a target textual segment of a target participant, and (ii) one or more peripheral semantic embedding vectors, respectively corresponding to one or more peripheral textual segments of the plurality of textual segments; and inferring a composite machine-learning (ML)-based model on the semantic vector set, to classify a sentiment of the target participant, as expressed in the target textual segment.

The invention relates to the technical field of speech synthesis, in particular to a speech synthesis-based corpus expansion method, system and device and a medium, and the method comprises the steps: carrying out the preprocessing including data annotation based on the collected audio and corresponding text of a target speaker; extracting acoustic features from the preprocessed audio; on the basis of a pre-trained acoustic model, performing personalized fine tuning by using the annotation data and the acoustic features, and training personalized acoustic models of a plurality of speakers at the same time through multi-thread parallel computing; calling the trained personalized acoustic model, and synthesizing a voice corpus of the target text in combination with a vocoder; and based on the trained personalized acoustic model, continuously expanding the corpus by changing the text. The personalized voice corpus is quickly generated through a small number of voice samples, the data acquisition cost is remarkably reduced, and the corpus construction efficiency is improved.

Embodiments described herein provide a mechanism for replacing existing text encoders in text-to-image generation models with more powerful pre-trained language models. Specifically, a translation network is trained to map features from the pre-trained language model output into the space of the target text encoder. The training preserves the rich structure of the pre-trained language model while allowing it to operate within the text-to-image generation model. The resulting modularized text-to-image model receives prompt and generates an image representing the features contained in the prompt.

The invention discloses an automatic test method and device for control software, a medium and a product, and the method comprises the steps: obtaining an automatic test script which comprises a full-process test case executed when the control software is used for verifying a simulation verification board; and obtaining a current execution statement in the automatic test script, and identifying a to-be-operated target operation element and a target operation needing to be executed for the target operation element. And obtaining target text content matched with the target operation element, and positioning an element screen coordinate of the target operation element according to the target text content. And after executing the target operation in the target graphical user interface according to the element screen coordinates, returning to execute the operations of sequentially obtaining the current execution statements in the automatic test script, obtaining the updated target graphical user interface when it is determined that iteration is ended, and verifying the execution correctness of the automatic test script. According to the technical scheme, the accuracy and execution efficiency of automatic testing through the control software are improved.

A multimodal-based contrastive decoding hallucination mitigation method and apparatus, and a terminal. The method comprises: acquiring target initial data, and performing multimodal perturbation processing on the target initial data to obtain target perturbed data, the target initial data comprising a target high-definition image and a target text prompt corresponding thereto; inputting the target initial data and the target perturbed data into a target model to obtain an initial output and a hallucination output, the target model being a large-scale visual language model; and performing contrastive decoding on the initial output and the hallucination output to obtain a target output. According to the solution, multimodal noise is added and compared with an initial output, thereby calibrating the prediction of the model and reducing the possibility of producing erroneous information.

The embodiment of the invention provides an audio synthesis method and device, a medium and equipment, and relates to the technical field of speech synthesis. The method comprises the steps of obtaining a target text to be subjected to audio synthesis; splitting the target text into a plurality of text units with complete semantics to obtain a task sequence; for the ith text unit in the task sequence, scheduling influence factors are obtained before the audio synthesis task is executed, the scheduling influence factors comprise application layer semantic information and / or equipment real-time performance indexes, and i is a positive integer; determining a target synthetic link from a cloud synthetic link and an end-side synthetic link based on the scheduling influence factors; and completing audio synthesis of the ith text unit through the target synthesis link. According to the scheme provided by the embodiment of the invention, in the audio synthesis process of the target text, adaptive switching between the cloud audio synthesis link and the end-side audio synthesis link can be realized in a low-delay manner.

The invention relates to the technical field of target detection, in particular to a single-domain generalization target detection method and device, electronic equipment and a storage medium, when it is detected that no target domain potential target is found, a target image is acquired, a text prompt template is constructed to obtain preliminary text description, an image classification model is pre-trained to extract image features of a target image source field, and the target image source field image features are extracted; and pre-training the text classification model to extract a target domain text prompt, obtaining a target domain style parameter, and determining a target domain visual feature in combination with the source domain image feature. And finally, inputting the visual features of the target field and the text prompt into a preset language-image positioning model, so that the preset language-image positioning model outputs a prediction detection frame corresponding to the potential target and a target text prompt. According to the method, the detection capability of the model in the unseen target domain is improved, and more accurate single-domain generalization target detection is realized.

A method of recognizing a speech, a device, and a medium. The method includes: processing, by using an acoustic model, speech data to be recognized and a first text segment obtained by recognition to obtain respective acoustic probabilities of a plurality of candidate text segments; processing the first text segment by using a first language sub-model to obtain respective initial language probabilities of the plurality of candidate text segments; processing the first text segment by using a constraint sub-model to obtain extendibility relationships of the plurality of candidate text segments with respect to the first text segment; adjusting the initial language probabilities of the candidate text segments according to the extendibility relationships to obtain respective first language probabilities of the plurality of candidate text segments; and determining a target text segment from the plurality of candidate text segments according to the first language probabilities and the acoustic probabilities.

The invention discloses a super-long text generation method and device, equipment and a storage medium, and the method comprises the steps: responding to a document generation instruction, and generating an initial text outline of a target text through a large model; wherein the initial document outline comprises a plurality of chapter titles; based on the keyword information of the initial document outline, the text complexity of the target text is obtained through calculation, and the text complexity is associated with the subject breadth of keywords and the expected word number of the target text; if the text complexity of the target text is greater than a preset threshold value, determining the initial text outline as a target text outline; and on the basis of the target text outline, the text content corresponding to each chapter title is generated in parallel and spliced to obtain the target text, so that the generation efficiency, the text coherence and the overall text quality of the generated super-long text can be improved, and the generation cost of the super-long text is reduced.

The invention provides a speech synthesis method and a related device, and relates to the technical field of speech synthesis. The speech synthesis method comprises the following steps: acquiring a first emotion feature of a target historical interaction speech; predicting a second emotion feature of a target voice to be generated according to the first emotion feature, a historical interaction text and a target text; wherein the historical interaction text is a text corresponding to the target historical interaction voice, the target text is a reply text generated based on an input voice in the latest round of voice interaction, and the target voice is a reply voice to be generated in the latest round of voice interaction; and generating the target voice according to the first emotion feature, the historical interaction text, the second emotion feature and the target text. According to the technical scheme provided by the invention, the problem that the emotional rhythm of the reply voice does not accord with the current context when the reply voice is generated based on the reply text in the prior art can be solved.

The invention provides a sample generation method, a text classification model training method and device and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of text classification, natural language processing and deep learning. According to the implementation scheme, a target text unit is recognized from an original sample set used for training a text classification model; determining a target category of an enhanced sample to be generated for the target text unit in the plurality of categories; obtaining a first semantic scene rule corresponding to a target category of the target text unit; and based on the target text unit and the first semantic scene rule, utilizing the large model to generate a first enhanced sample for training a text classification model.