200 results about "Source text" patented technology

The invention relates to the technical field of equipment maintenance and overhaul decision, and discloses a knowledge graph driven two-stage classification equipment condition-based overhaul decision method, which comprises the following steps: acquiring multi-source text data; performing first-stage classification on the equipment based on the equipment importance evaluation model, and performing second-stage classification on the parts based on the maintenance strategy; a device entity, a fault mode entity and a maintenance measure entity are taken as knowledge ontologies, state variable nodes are embedded, and a knowledge graph fusing state variables is constructed by using a long-short-term memory network; in combination with a semantic context sensing mechanism and a graph structure stability constraint mechanism, outputting candidate fault nodes; candidate maintenance measures corresponding to the candidate fault nodes are retrieved in the knowledge graph, candidate maintenance measure verification is carried out in combination with soft constraints and hard constraints, and a maintenance measure list is converted into an equipment maintenance decision scheme by utilizing an execution arrangement generator, so that intelligence of the maintenance decision scheme is realized in combination with the knowledge graph; and decision support is provided for maintenance personnel.

The invention relates to the technical field of building information, and provides a knowledge base construction and retrieval method and system based on a multi-source text in the building field, and the method comprises the following steps: a knowledge base construction stage: constructing a multi-dimensional metadata feature vector for a multivariate text based on a standard classification index table; the method comprises the following steps of: converting and segmenting a document, splicing an end clause with all superior title texts by utilizing a context inheritance algorithm to form a text unit with complete semantics, and performing dynamic filtering based on an analyzed query intention and a metadata vector at a user retrieval stage; then, in the screening set, performing fusion calculation on semantic vector similarity, keyword matching degree and authority offset weight based on effectiveness attribute and implementation time, and performing mixed retrieval and reordering on the text units; and finally selecting a text unit according to a sorting result and inputting the text unit into the large language model to generate answers. According to the method, high-precision and high-compliance intelligent retrieval and question answering of building domain knowledge are realized.

The invention relates to the technical field of prompt optimization engineering, in particular to an embedded clustering multi-path recall large model question and answer method, system and device and a medium, and the method comprises the following steps: preprocessing a multi-source text to generate an associated paragraph, and converting the associated paragraph into a semantic vector; executing clustering and adjusting a clustering center based on a matching strategy to form a vector library; receiving queries, converting the queries into semantic vectors and retrieving related units in parallel; the recall result is comprehensively evaluated on the basis of a credibility perception attention mechanism in combination with semantic matching and timeliness score, the semantic matching score is calculated by a local bge-ryanker-v2-m3 model, the timeliness score is quantized through a date difference attenuation function, and finally high-quality content is weighted and screened to be input into a large language model to generate question and answer output. And the result accuracy, timeliness and service adaptability are improved.

The invention discloses a multi-language text self-adaptive configuration method and electronic equipment, and relates to the technical field of computers, and the method comprises the following steps: obtaining a source text set in response to a translation instruction received when a page runs, and translating the source text set according to a target language identifier and a constraint strategy to obtain a translated text set; according to a constraint strategy, carrying out adaptability detection on each translation in the translation set to obtain an adaptive set and a non-adaptive set; performing semantic rewriting on each non-adaptive translation in the non-adaptive set to obtain a target candidate set; and aggregating the adaptation set and the target candidate set in the same rendering frame, and rendering and displaying the adaptation set and the target candidate set in batches, so that the problems of lack of multi-language dynamic adaptation capability and insufficient semantic equivalent compression are solved, the accumulated layout offset is remarkably reduced on the premise of ensuring semantic integrity and readability, and the layout efficiency is improved. And the page stability and the user experience are improved.

The invention discloses a structured data storage method based on natural language transformation, which comprises the following steps of: a system initialization configuration stage: deploying a protocol adapter in a local memory of a PC (Personal Computer) client, and loading natural language processing pipeline configuration parameters; a heterogeneous data acquisition stage: capturing a multi-source text data stream through the protocol adapter, uniformly converting the multi-source text data stream into a standardized data packet, and sending the standardized data packet to a message queue theme; a text cleaning stage: a named entity recognition stage: inputting the pure text data into an NER module deployed with a language model loader; in the conditional feature extraction stage, feature vectors are generated for texts meeting preset conditions on the basis of entity type tags in the entity recognition result; and a consistent storage stage: inserting the entity identification results into a relational database in batches, and updating the entity mapping relationship in the cache. According to the method, the intelligent level of cache management is remarkably improved, and the access fluency of the key data of the user is guaranteed.

A computer-implemented process is programmed to programmatically receive at a first computer a digital electronic object comprising a source text having been composed at a second computer, send instructions to the second computer for presenting filters via a user interface, which are programmed to adjust the source text when they are selected and executed, receive a selection of a first filter, generate an output set of suggestions based on executing the first filter over the source text, transmit the output set of suggestions to the second computer, receive a specification to apply the suggestions, and in response, automatically apply all the suggestions to the source text and transmit updated presentation instructions to the second computer which when rendered using the second computer cause displaying an updated text with all the suggestions having been applied to the source text.

A method implements pre-trained large language model driven bug localization. The method includes receiving a report and applying a fine-tuned language model to report text from the report, to source text from a source file of a set of source files, and to commit text from a commit of a set of commits to respectively generate a report vector, a source vector, and a commit vector from the fine-tuned language model. The method further includes applying a similarity model to the report vector and the source vector to generate a report source score and includes applying the similarity model to the report vector and the commit vector to generate a report commit score. The method further includes applying a ranking model to the report source score and the report commit score to identify the source file corresponding to the report and includes presenting the source file responsive to the report.

In accordance with the described techniques, a system receives a plurality of facets describing language-agnostic aspects of language translation, a translation document describing language-specific rules for translating from a source language to a target language, and a source text in the source language. Using one or more machine learning models, a plurality of guidelines are extracted from the translation document and assigned to respective facets of the plurality of facets. The system translates the source text to a translated text in the target language using one or more machine learning models conditioned on the plurality of guidelines assigned to the respective facets.

The invention discloses a 7 * 24-hour emergency response intelligent advertisement automatic adjustment system, which intelligently judges response rhythm and strategy strength by monitoring multi-source text data, identifying emergencies and influence degrees thereof in real time and combining a dynamic adjustable trigger threshold mechanism and event popularity prediction based on a time sequence model. For similar historical events, the system supports a rapid migration response strategy, and the decision-making efficiency is remarkably improved. And the strategy reasoning module fuses the rule and the machine learning model to generate an optimal advertisement putting scheme with multi-dimensional scoring, and the advertisement scheduling module automatically executes strategy adjustment. And the feedback learning module performs iterative updating on the strategy model by utilizing advertisement effect real-time data based on a reinforcement learning algorithm, and realizes closed-loop self-learning of'prediction-execution-evaluation-re-optimization '.

The invention provides a large model context learning machine translation method based on word granularity alignment, which relates to the field of natural language processing, and comprises the following steps: an external knowledge assisting stage: performing multi-level retrieval matching on a source text word alignment set; a large model translation stage: obtaining a large model translation set, and taking the large model translation set as one of candidate translations; in the post-selection stage, the obtained source text word alignment set, the obtained external dictionary word alignment set, the obtained entity library alignment set, the obtained embedding representation of the auxiliary translation set and the obtained large model translation set are subjected to similarity calculation scoring for multiple times, and screening is conducted according to similarity scores to obtain a candidate word alignment set; a prompt template is designed according to a task, a source text word alignment set and a candidate word alignment set are put into the prompt template, and a highly-aligned external word alignment set is used for large model context learning to generate an optimal translation result; according to the method, various translation errors of a large model in a low-resource environment are relieved.

Enumerated source text passages may be determined based on one or more source text documents. The enumerated source text passages may include source text passage identifiers uniquely identifying the passages. A novel text passage including novel text portions may be determined based on a query and the enumerated source text passages. One or more of the novel text portions may be verified by a large language model to produce text verification information. A novel text generation message including novel text generated by the large language model may be determined based on the text verification information and sent to a client machine.

The invention discloses a photovoltaic construction professional knowledge base construction and updating method based on an NLP technology. The method comprises the following steps: firstly, acquiring a multi-source text from multiple channels; photovoltaic domain entity recognition is completed by adopting a named entity recognition technology after preprocessing, an entity relationship is extracted through dependency syntax and semantic role labeling, and a knowledge triple is generated; and then performing unified modeling on entity category levels and relationship types based on the ontology, storing a triple into a graph database, storing detailed attributes and business process data of entities and relationships into a relational database, and realizing interconnection of the two databases by a bidirectional URI mapping mechanism. In the updating stage, minute-level knowledge updating is achieved through increment monitoring, entity alignment, conflict resolution and version control. And finally, performing quantitative evaluation on knowledge quality, and performing feedback by a visual instrument panel to form closed-loop optimization. Real-time and reliable knowledge support can be provided for planning, design, construction and operation and maintenance of a photovoltaic project through knowledge base entity recognition query constructed by the method.

A computer system generates descriptions of image-text misalignments. The system includes one or more processors and models for generating textual and visual descriptions of misalignments between a source text string and a source image. The textual description identifies misaligned text segments, while the visual description may include bounding boxes indicating the location of the misalignment. This system automatically generates synthetic image-text misalignment training examples and feedback, which includes generating misalignment captions and visual bounding box labels.

The invention discloses a multi-dimensional text data classification processing method and system, and relates to the field of text data classification processing. Collecting structured, unstructured and semi-structured texts, and carrying out denoising, word segmentation, standardization, missing value processing and abnormal text processing; extracting features from semantics, grammar, emotion and domain exclusive dimensions; optimizing features through standardization, correlation analysis, PCA dimension reduction and feature importance ranking; a'pre-training language model + traditional machine learning model 'fusion framework is constructed and trained; real-time and batch reasoning is supported; evaluating a classification result and feeding back a to-be-optimized sample; when the to-be-optimized sample reaches the standard or the performance is reduced, model iteration is started, and the model is updated after test verification. According to the method, the multi-source text preprocessing suitability and the data quality are improved, the classification accuracy and the field suitability are enhanced through multi-dimensional feature fusion, the performance and efficiency are optimized through the fusion model and incremental training, data changes are continuously adapted, and an efficient and reliable classification scheme is provided.

The invention discloses a text translation quality assessment method and device, computing equipment, a storage medium and a computer program product. The method comprises the steps of obtaining a translation pair formed by a to-be-assessed source text and a translation text; executing first-layer evaluation: evaluating the translation pair based on rule detection to obtain an evaluation result; executing second-layer evaluation, including evaluating the translation pair based on a thinking chain reasoning mechanism of a large language model to obtain an evaluation result; and executing fusion processing, including fusing the obtained evaluation results to obtain a target evaluation result representing the evaluation quality of the translation pair. Therefore, the translation quality evaluation method and device achieve the balance of high efficiency, high accuracy and interpretability of translation quality evaluation, and are particularly suitable for professional scenes such as game texts.

The invention provides an intelligent processing system for text translation and semantic optimization, and relates to the field of electric digital data processing, the intelligent processing system comprises an information interaction module, a text translation module, a semantic understanding module and a semantic optimization module, the information interaction module is used for inputting a source text and outputting a translation result, the text translation module is used for performing language conversion on a source text to obtain a target text, the semantic understanding module is used for constructing a semantic structure of the target text, and the semantic optimization module is used for performing retouching optimization on the target text based on the semantic structure; according to the system, multiple structured scoring mechanisms are introduced, so that translation and optimization cooperative control is realized, and the accuracy, naturalness and controllability of translated texts are improved.

The invention discloses a similarity calculation method based on semantic editing fusion. The method comprises the following steps: calculating global semantic similarity of a source text and a target text by utilizing a semantic coding model; obtaining a first keyword list of the source text and a second keyword list of the target text through word segmentation processing, taking each first keyword in the first keyword list as a target word, and respectively forming a plurality of to-be-compared word pairs with a corresponding word in the second keyword list and a plurality of adjacent second keywords; based on the to-be-compared word pairs, calculating the semantic similarity between each group of target words and the corresponding words by utilizing a semantic coding model, and determining the editing distance between the source text and the target text; and determining the local semantic similarity of the source text and the target text according to the editing distance, and combining the global semantic similarity to obtain the final text matching similarity. According to the method, the problem that text matching only depends on character-level surface matching and neglects semantic association between word pairs is solved, and the text matching precision is improved.

The invention provides a method for generating high-quality instruction data in the ship manufacturing field based on a knowledge graph. The method comprises the following steps: (1) collecting and preprocessing a multi-source text; (2) knowledge extraction and graph construction; (3) knowledge point understanding evaluation and marking; (4) extracting constrained sub-graphs; (5) generating a multi-type instruction-response pair; (6) data source monitoring and map increment updating; (7) the instruction-response pair increment is updated and audited; and (8) performing multi-dimensional quality evaluation and output on the instruction data. The invention aims to solve the problems of high training data construction cost, high illusion rate, low coverage and poor timeliness in a large language model fine tuning process in the ship manufacturing field.

The invention discloses a user demand insight positioning method and system based on AI driving, relates to the field of artificial intelligence and big data analysis, and solves the technical problem that it is difficult to accurately recognize user demands and respond to the user demands in real time through dynamic analysis of multi-source text data and construction of a knowledge graph. Comprising the steps that text data are collected, structured data are generated through preprocessing, and a structured data unit is generated; calculating text vocabulary diversity and syntactic complexity indexes, inputting the text vocabulary diversity and syntactic complexity indexes into a pre-training regression model to obtain a dynamic character threshold value, encoding short / long texts by adopting a lightweight graph neural network and a hierarchical Transform, and extracting core demand elements; a dynamic time sequence knowledge graph is constructed, a time sequence edge weight updating algorithm is introduced to adjust node association strength, and cross-domain association is mined; and calculating a user demand intensity evaluation value based on an entropy weight method in combination with the inventory pressure factor and the group influence factor, thereby realizing inventory scheduling and personalized recommendation.

Provided in embodiments of the present disclosure are a content presentation method and apparatus, a device, and a storage medium. The method comprises: in response to a request for source text content, on the basis of analysis for the source text content, determining a first analysis result at least indicating summary information in the source text content and at least one second analysis result indicating at least one content unit of the source text content; acquiring respective role configurations of a plurality of speakers corresponding to the source text content; and generating first dialog content of the plurality of speakers on the basis of the first analysis result, the at least one second analysis result, and the role configurations of the plurality of speakers, wherein the first dialog content comprises a first dialog segment and at least one second dialog segment following the first dialog segment, the first dialog segment is associated with the summary information, and the at least one second dialog segment is separately associated with the at least one content unit.

This invention relates to the field of knowledge base construction technology, specifically disclosing a method and system for constructing an industry knowledge base based on text-based data synthesis. The method includes entity recognition of multi-source text data, constructing a knowledge point sequence, and marking key knowledge points in the knowledge point sequence; expanding the key knowledge points based on a large language model to generate an expanded knowledge point set; constructing a knowledge graph based on the expanded knowledge point set, and constructing a preset number of knowledge connection paths in the knowledge graph; classifying the knowledge connection paths and inserting them as knowledge skeletons into the industry knowledge base. When dealing with massive amounts of multi-source text data, this invention expands the keywords based on a large language model to obtain expanded knowledge points, then constructs a knowledge graph, extracts node paths from the knowledge graph, uses the node paths as knowledge skeletons, classifies them, and stores them in the knowledge base. While the resulting knowledge base still contains a large amount of information, it significantly simplifies its size.

The application discloses a kind of general pre-training graph structure large model construction method and system, belong to graph computing technical field, including: from the sample set of text attribute subgraph and node text attribute contained in multiple source text attribute graph data is constructed;Graph structure encoder is pre-trained using the reversible serialization mode based on Euler path improvement mode, and graph structure representation is obtained;Text feature encoder is used to encode node text attribute into text semantic representation, after graph structure representation and text semantic representation are mapped to the same alignment space by mapping network, based on text semantic representation and graph structure representation, construct joint global representation alignment task, substructure-phrase level local semantic alignment task, and graph to text reconstruction task, train mapping network, obtain general pre-training graph structure large model, so it can effectively bridge the modal gap between graph structure and text semantic, significantly improve the generalization ability and reasoning reliability of model in downstream task.

The invention relates to the technical field of natural language processing, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a text style conversion control method, device and equipment and a medium. Performing text word segmentation on the source text and the target style description text to obtain a corresponding source text sequence and a corresponding target style sequence; performing style feature extraction on the source text sequence and the target style sequence to obtain corresponding source text style features and target style features; determining a target style feature conversion parameter according to the source text style feature and the target style feature; performing text coding on the source text sequence to obtain a source coded text sequence; and performing style conversion on the source coding text sequence according to the target style feature conversion parameter to obtain a target style text. The text style conversion efficiency and style control granularity can be improved.

The present disclosure provides a method for knowledge distillation, an apparatus, an electronic device, a medium and a computer program product. The method includes: acquiring a training source text and a standard translation text corresponding to the training source text; inputting the training source text into a teacher translation model and a student translation model separately, to obtain a teacher distribution output by the teacher translation model and a student distribution output by the student translation model; obtaining a standard translation distribution according to the standard translation text and the training source text; and performing iterative training on the student translation model according to the teacher distribution, the student distribution, and the standard translation distribution, to obtain a target machine translation model.

A system and method for transforming text within documents using, such as by using large language models (LLMs). Users can select source text from a source document, in response to which a painting configuration is identified or generated based on the source text, such as by providing the source text and a source prompt to a large language model to produce source output, and selecting or generating the painting configuration based on the source output. The user can select destination text, in response to which the painting configuration is applied to the destination text, such as by selecting or generating a destination action definition based on the painting configuration and the destination text, and providing the destination action definition to a large language model to produce destination output. The destination text may be replaced with the destination output, or output derived therefrom. In this way, the system can extract a variety of sophisticated properties, such as style or tone, from user-selected text source text, and apply those properties to user-selected destination text, with minimal user input.

The invention discloses a gas hidden danger investigation semantic element extraction method, which comprises the following steps: inputting a gas hidden danger investigation field knowledge text, and carrying out hidden danger knowledge segment segmentation on the knowledge text; constructing a candidate hidden danger semantic element set for the segmented hidden danger knowledge segments; calculating semantic element space risk measurement based on the hidden danger semantic element set to obtain a risk score; and recognizing the hidden danger semantic elements based on the risk scores. According to the method, the accuracy of gas hidden danger recognition can be improved, the troubleshooting accuracy, efficiency and risk disposal priority are remarkably improved, and the method is suitable for knowledge extraction and hidden danger recognition scenes of various multi-source texts.

The application discloses a cross-domain named entity recognition method based on an autoregressive model, and comprises the following steps: S1, encoding an input sequence; S2, encoding a label through a label encoder; S3, obtaining label background information; S4, obtaining label context information; S5, connecting the label background information to the input sequence and connecting the label context information to the predicted named entity label as final label perception information z i , and finally obtaining a final sequence representation u; the application provides a cross-domain named entity recognition method based on an autoregressive model, which improves the relationship between a source text and its named entity label, improves the portability of label information, and helps the model to promote domain adaptation.

The invention discloses a two-stage intelligent text clustering method based on large language model guidance, and relates to the technical field of artificial intelligence, natural language processing and machine learning, and the method comprises the steps: converting a to-be-processed text into a multi-source text vector set, and determining a standardized unified text representation vector set in combination with information entropy; determining a pre-clustering result through the dynamically calculated initial clustering number; calculating an inter-cluster Ward distance based on a pre-clustering result, introducing cluster size weighted correction, and constructing a clustering tree; a candidate cluster number set is determined based on the MDL principle, cluster pairs to be combined are extracted by accessing a cluster tree in a reverse order, whether the sample pairs belong to the same cluster or not is judged through a large language model, and a weighted F-beta score is calculated to determine the optimal cluster number; and intercepting a target clustering result according to the optimal clustering number and generating a clustering result. According to the method, the calculation complexity is reduced to O (n), the consistency of a clustering result and human cognition is improved, and the method is suitable for rapid and accurate clustering of large-scale text data.

This invention discloses a sentence-level question generation method based on syntactic-aware prompt learning. First, a bidirectional syntactic dependency graph is constructed based on a given sentence. Its semantic representation is obtained through a relation-aware attention graph encoder. The encoded vectors are then input into a softmax layer, and the top k vectors are selected as continuous prompts based on probability. The prompts are concatenated to the given source text and the answer using prefix adjustment, and then input into a BERT model for encoding. The encoded result is then fed into a Transformer model for decoding. At each time step of decoding, the syntactic dependency information of the generated text sequence is modeled. This information, combined with the syntactic dependency information of the source sentence, determines the parts that the decoder needs to focus on, assisting in the generation of the current word. Simultaneously, a copying mechanism is introduced to handle situations where the generated word is not in the question vocabulary, allowing the model to directly copy words from the source text.

[Problem] To compare information disseminated in an unspecified field with information extracted from other information sources. [Solution] A program for operating a computer causes a processor of the computer to execute: a step for receiving designation of a text; a step for generating a prompt including an instruction to extract, from an information source usable by an artificial intelligence system, at least one source text including a description similar to a designated text which is the text that has been designated, and to output information on the source text, and an instruction to compare the extracted source text with the designated text that has been designated; and a step for, on the basis of an answer obtained from a large-scale language model by inputting the prompt to the large-scale language model provided by the artificial intelligence system, presenting, to a user, information on the source text and a result of the comparison between the designated text and the source text.