263 results about "Source text" patented technology

Methods and systems for retrieval-augmented generation are described. Responsive to a user input, an input embedding associated with the user input is obtained. A synthetic question embedding is retrieved from an embeddings database, based on a similarity to the input embedding. The synthetic question embedding is used to obtain a relevant source text based on a stored mapping between the synthetic question embedding and the source text. A prompt is provided to a large language model (LLM) to generate and display a textual response to the user input, based on the user input and the source text. The disclosed methods and systems effectively narrow the pool of source documents based on similarity measures between the user input embedding and the synthetic question embedding, to enable the retrieval of more relevant sources for use in response generation.

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 method and system for generating an EBT scene of a pilot based on a text and flight data, and the method comprises the steps: extracting threat error items in the operation of the pilot from a multi-source text through employing a scene perception word frequency-inverse document frequency method, and generating text analysis; a method of combining time feature attention, convolutional self-encoding and a long short-term memory network is utilized to comprehensively analyze flight data of daily routes and simulator training, dynamic features and potential risk factors in pilot operation are extracted, and flight data analysis is generated; in combination with text data analysis, flight data analysis and a scene element library, training scene elements are generated by adopting a hierarchical rule structure condition generative adversarial network method, and a teacher performs screening and combination to generate a personalized training scene. Through multi-source data fusion and data-driven analysis, a pilot personalized training scene is generated, the pertinence and effectiveness of training are improved, the use of training resources is optimized, and the overall flight safety and training quality are improved.

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.

The invention relates to the technical field of intelligent retrieval of papers, and discloses a paper review expert intelligent recommendation method and system.According to the method, word paraphrasing is carried out on review expert text information and to-be-reviewed paper text information, semantic connotation supplementation and extension can be carried out on text information with limited information amount, and therefore the content of paper review experts is improved. Richer and more useful information is provided; multi-source information fusion is carried out on the expanded text information, the expanded rich multi-source text information can be summarized, extracted and summarized, and comprehensive text information with sufficient information content and high distinction degree is provided; and performing overall vectorization on the comprehensive text information, performing similarity calculation and sorting on the review expert text vector and the to-be-reviewed paper text vector, and screening out review expert information with relatively high similarity as output. The method can effectively improve the matching precision of the paper'small peer 'review experts, reduces the rejection rate caused by inconsistent research directions, and provides technical support for improving the review quality and shortening the review period.

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.

Digital video editing techniques are described that are based on a target digital image. In one or more implementations, inputs are received. The inputs include a target text prompt, a target digital image depicting a target object, and a source digital video having a plurality of frames depicting a source object. Regions-of-interest are identified in the plurality of frames of the source digital video, respectively, based on the target text prompt and the target digital image using a machine-learning model, e.g., a diffusion model. A plurality of frames of a target digital video are generated as having the target object using a generative machine-learning model. The generating is based on the regions-of-interest, the target digital image, the source digital video, and a source text prompt describing the source digital video.

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 data element retrieval and archiving system and method for text natural language understanding, and relates to the technical field of data element retrieval and archiving. A multi-source text is obtained, the multi-source text is preprocessed, and an initial text library is established; the complexity of the text is analyzed based on the text data element information and the text structure information, the complexity level of the text is determined, and then the texts with different complexity levels are archived; receiving a keyword input by a user, performing text retrieval according to a keyword expansion strategy and data element tracking, and further generating a text recommendation folder; and analyzing the user access data of each text in the text recommendation folder, and performing text screening according to the texts concerned by the user to generate a secondary text recommendation folder. Information is efficiently extracted from multi-source text data, and accurate and valuable text content is provided for users through intelligent retrieval and personalized recommendation.

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 discloses a biomedical text pre-training generation method, which comprises the following steps of: dividing an input biomedical text sentence into smaller fragments or word blocks, and performing processing through mark embedding, fragment embedding and position embedding to obtain a pre-processed text; generating a text embedding vector from the preprocessed text through a self-attention mechanism; the text embedded vectors are clustered by adopting a clustering algorithm, finally all the embedded vectors are divided into clusters, and each cluster is represented by a centroid; selecting m vectors closest to the center in each cluster, and arranging the m vectors according to an appearance sequence in the document; and the selected context embedding vector is coded by BertSum, decoding is carried out through six layers of Random Transformers, and abstract extraction is converted into abstract generation. According to the method, the higher semantic similarity between the extracted text and the source text is ensured, and the overall semantic consistency between the candidate abstracts and the original document is emphasized.

The invention discloses a translation quality evaluation method and device, equipment and a storage medium, and the method comprises the steps: obtaining source texts of a plurality of fields and target translations obtained after the source texts are translated, and constructing a training set and a corpus; dividing the training set according to fields to obtain a plurality of groups of training subsets; respectively training a preset neural network through each group of training subsets, calculating a loss value according to a predicted quality score of the preset neural network and a corresponding actual quality score, updating network parameters of the preset neural network through the loss value until the preset neural network converges, and obtaining a translation quality evaluation model corresponding to each field; determining a field category to which the to-be-evaluated text belongs through a corpus; and according to the domain category, calling a corresponding translation quality evaluation model to carry out translation quality evaluation on the to-be-evaluated text to obtain a translation quality evaluation result of the to-be-evaluated text. According to the invention, the method improves the discrimination capability of the vocabularies in the professional field, and solves a problem that the evaluation of a general model in the professional field is not reliable.

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.

The invention discloses a text dialogue method and device suitable for multiple languages, terminal equipment and a storage medium, after a source text input by a user in a current dialogue is obtained, a target language corresponding to the source text can be accurately recognized based on a phrase structure and a sentence structure of the source text, and therefore the user can select the target language based on the recognized target language. And a corresponding grammar rule can be selected to generate a subsequent reply text. In addition, the user intention and context information can be deeply understood based on the extracted local features, global features and dialogue features, so that the reply text conforming to the language expected by the user is generated, and the generated reply text is correct in grammar based on the language of the current input text and the grammar rule of the language. And the expression is natural and smooth, the stiff or incoherent condition is avoided, the accuracy and naturalness of the reply text in the dialogue process are improved, the multilingual text dialogue is realized, and the dialogue experience of the user is optimized.

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.

A method of facilitating consumption of online content includes receiving source text for a source article to be translated, the source text being in a source language. The source language for the source text and the target language to which the source text is to be translated are each identified. The source text, the source language, and the target language are each provided to a machine translation model which automatically generates translated text in the target language from the source text. The translated text is provided as input to a generative language model which generates summary text in the target language from the translated text. The summary text is provided to a text-to-speech model which generates summary audio from the summary text. The summary text and summary audio are then sent to a user interface via which the summary text is displayed, and playback of the summary audio is enabled.

The invention discloses a translation quality evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-evaluated task; for each to-be-evaluated translated sentence, evaluating the target to-be-evaluated translated sentence based on a target reference translated sentence set associated with the target source text statement to obtain a sentence level evaluation attribute corresponding to the target to-be-evaluated translated sentence; evaluating each to-be-evaluated segmented word in the target to-be-evaluated translated sentence based on a reference translated word in the target reference translated sentence set to obtain a word level evaluation attribute corresponding to each to-be-evaluated segmented word; and according to the sentence level evaluation attribute of each to-be-evaluated translated sentence and the word level evaluation attribute of each to-be-evaluated segmented word, generating a translated text evaluation report corresponding to the to-be-evaluated task. The effect of more objectively and accurately evaluating the overall translation quality of the translated text and the translation quality of the proprietary entity is achieved.

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 translation method, comprising: acquiring target source text information and translation requirement information corresponding to the target source text information (S210); searching historical translation records for information related to the target source text information, so as to obtain first reference translation information (S220); combining the translation requirement information with the first reference translation information and generating translation prompt information (S230); and inputting the translation prompt information into a language model, and, on the basis of an output result of the language model, obtaining target translation information corresponding to the target source text information (S240).

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 disclosure herein describes interpreting attention-based decisions of summarization outputs generated by a deep learning model. A decision interpretation model obtains attention values defining connections between input tokens associated with a source text and output tokens for a selected portion of a summary associated with the source text. The input tokens having the highest attention values indicating the strongest connections between the input tokens of the source text and an output token of the summary are selected as primary tokens. A semantic similarity between the primary tokens for each attention head and an output token is calculated. The model selects the primary tokens having the closest semantic similarity with the summary portion. A visual cue is generated on or within a portion of the source text corresponding to the primary tokens. The visual cue identifies dominant words in the source text used to explain the summary portion.

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 present invention relates to the technical field of language translation, and specifically to a method and system for optimizing translation accuracy based on artificial intelligence, including: obtaining a source language text, performing a sentence segmentation operation on the source language text to obtain a source language sentence sequence of the source text, and performing a word segmentation operation on each source language sentence in the source language sentence sequence to obtain a word sequence of the source language sentence; performing sentiment classification on each source language sentence in the source language sentence sequence based on a pre-trained first sentiment classification model. The present invention performs sentiment classification on the source language sentence and the target language sentence respectively based on the first sentiment classification model and the second sentiment classification model, and calculates the similarity between them to ensure that the translated sentence is consistent in sentiment, which helps to avoid the problem of sentiment deviation in the process of machine translation, especially for some texts with strong sentiment colors, and ensures that the sentiment information of the translation can be conveyed correctly.

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.