46 results about "Correction text" patented technology

The invention provides a test case self-correction Text-to-SQL method and system and a medium. The method comprises the steps that diversified SQL test cases are generated through a large language model; and analyzing feedback information of the SQL test case, and generating an SQL query statement through iterative correction. According to the method and the device, the test case which is as accurate as possible can be provided and is used for pointedly testing and checking errors possibly existing in the SQL query statements, so that the accuracy of generating the SQL query statements is improved; according to the method, the semantic error which is difficult to perceive in the SQL query statement can be corrected through a design framework of SQL generation-test case feedback-SQL correction, so that semantic understanding deviation of an original problem is avoided as far as possible, and the situation that a query result of the generated SQL query statement does not conform to the requirement of the original problem, so that a subsequent data analysis link is influenced is avoided.

Provided in the present disclosure are a language model training method and apparatus, and a computer program product. The method comprises: acquiring target text corresponding to one or more rounds of dialogues between a preset language model and a user, wherein the target text comprises user input text and model reply text; analyzing the target text by means of the preset language model, so as to obtain a user feedback result of the user for the one or more rounds of dialogues; on the basis of the user feedback result, acquiring a model reply correction text for the user input text in the target text; constructing a model training sample on the basis of the target text, the user feedback result and the model reply correction text; and on the basis of the model training sample, iteratively training the preset language model. In the method and related device provided in the present disclosure, model training text can be acquired by means of reducing the complexity of user feedback operations, and automatic iterative training is performed on a preset language model, such that the preset language model can output more accurate text that meets the user requirements.

The invention discloses a Chinese error correction method and system based on multi-agent collaboration and retrieval enhancement, and belongs to the technical field of network space security. The method comprises the following steps: step 1, constructing a plurality of Chinese error correction expert agents and one Chinese error correction judgment agent based on a large language model and an agent prompt project; step 2, establishing an error correction text knowledge base, converting corpora in the knowledge base into a vector database, and accessing a Chinese error correction expert agent and a judgment agent for information retrieval of the Chinese error correction expert agent and the judgment agent, enhancing context understanding ability and realizing a retrieval enhancement generation function; and step 3, designing a detection prompt, a discrimination prompt and a feedback prompt, constructing a multi-agent closed-loop feedback module, realizing information collaboration, step-by-step reasoning, result verification and iterative optimization of each agent, and completing a Chinese error correction task. According to the method, generalization error correction can be realized under the condition of limited training data, and the adaptability of the model to unknown errors is improved.

According to the voice transliteration text error correction method and device, the storage medium and the computer equipment, after the voice transliteration text is received in real time, the text is subjected to primary error detection, and the first error confidence coefficient is obtained; when the first error confidence coefficient is smaller than a fast error correction threshold value, the voice transcription text is directly output, and unnecessary error correction is avoided; otherwise, performing word-level rapid error correction on the voice transcription text to obtain a rapid error correction text, and performing secondary error detection on the rapid error correction text to obtain a second error confidence coefficient. Judging whether the second error confidence is smaller than a depth error correction threshold value or not; if yes, the rapid error correction text is directly output, and if not, semantic-level deep error correction is conducted on the rapid error correction text through a deep error correction model, and a deep error correction text is obtained and then output. Through a hierarchical error correction processing strategy, the method can be adapted to different service scenes, and meanwhile, dynamic balance between quality and efficiency of different service scenes can be realized by adjusting two error correction thresholds.

The embodiment of the invention provides a text error correction method, a text error correction device, electronic equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: inputting a target text into a text error detection model for text error detection to obtain text marking data, wherein the text marking data is used for marking correct characters and error characters in text characters; inputting the target text into a text error correction model based on the text marking data; extracting text character and pronunciation features of the text characters based on a character and pronunciation feature extraction sub-model; extracting text font features based on the font feature extraction sub-model, wherein the text font features comprise first mask features of error characters; extracting text semantic features based on the semantic feature extraction sub-model, wherein the text font features comprise second mask features of error characters; and performing text prediction on the text character pronunciation features, the text font features and the text semantic features based on the error correction sub-model to obtain a target error correction text. According to the embodiment of the invention, the error correction accuracy of the text can be improved.

A method, device, and computer-readable storage medium for predicting pronunciation of a text sample. The method includes selecting a predicted text sample corresponding to an audio sample, receiving a correction text sample corresponding to the audio sample, updating an encoding of allowable pronunciations of the correction text sample based on the predicted text sample and the audio sample, the updated encoding of allowable pronunciations of the correction text sample including a pronunciation of the predicted text sample, and predicting a pronunciation of the correction text sample based on the updated encoding of allowable pronunciations of the correction text sample.

The invention discloses a text information error correction model training system based on deep learning, and belongs to the technical field of text information processing. The system comprises a data input and preprocessing module which receives an original text and a correct text corresponding to the original text and processes the original text and the correct text into a wrong text, a standard correct text and a wrong correct text training pair; the model reasoning module receives the error text and outputs a corrected text; the result evaluation module analyzes the corrected text and the standard correct text, identifies error types and calculates quantitative indexes; the reinforcement learning signal generation module constructs reinforcement learning reward signals based on the error types and the quantitative indexes; the model optimization module updates model parameters based on the reinforcement learning reward signal and carries out training to obtain a new text information error correction model; and the model deployment module deploys the new text information error correction model as RESTful API service. The method improves the accuracy and efficiency of text error correction through the reinforcement learning optimization model, and is suitable for various scenes needing text error correction processing.

The application discloses a Chinese error correction method and system based on multi-agent cooperation and retrieval enhancement, and belongs to the technical field of network space security. The method comprises the following steps: 1, based on a large language model and an agent prompt engineering, a plurality of Chinese error correction expert agents and one Chinese error correction judge agent are constructed; 2, an error correction text knowledge base is built, corpus in the knowledge base is converted into a vector database, the Chinese error correction expert agent and the judge agent are accessed, and are used for information retrieval of the Chinese error correction expert agent and the judge agent, so that the context understanding capability is enhanced, and a retrieval enhancement generation function is realized; 3, detection prompts, discrimination prompts and feedback prompts are designed, a multi-agent closed-loop feedback module is constructed, information cooperation of each agent, step-by-step reasoning, result verification and iterative optimization are realized, and a Chinese error correction task is completed. The method can realize generalization error correction under the condition that the training data is limited, and improve the adaptability of the model to unknown errors.

The present application discloses a method, device, equipment and computer-readable storage medium for text correction, and belongs to the field of computer technology. The method includes: obtaining a first text and at least one dictionary of a corresponding field, the frequency of words in the dictionary meets the requirements; splitting the first text to obtain at least one character, matching the at least one character through the dictionary to obtain a matching character; obtaining a first matching relationship and a second matching relationship; determining a correction text according to the first matching relationship and the second matching relationship. The embodiment of the present application matches the characters in the first text to be corrected through at least one dictionary of the corresponding field to obtain matching characters, the frequency of words in the dictionary meets the requirements, and the matching relationship between any character in the first text and the matching character of the character is improved for the corresponding field. Then, the correction text is determined according to the matching relationship, which not only strengthens the text correction in the corresponding field, but also improves the accuracy of the text correction results.

The invention provides an image-text conversion correction method and device and a storage medium, and relates to the field of image processing. The method comprises the steps of obtaining a to-be-processed original image and inputting the original image into a preset graph-to-text model to generate text description; obtaining a simulation image based on the original image, the text description and a preset text graph model; performing slice analysis on the original image and the analog image to obtain image slice pairs and similarity data of each image slice pair; analyzing the image slice pair according to the similarity data to generate a corrected text; and correcting the text description through the correction text to obtain a target text. The text description is corrected by generating the text description through the original image, generating the analog image through the text description and comparing the original image with the analog image, so that the situations that object illusion exists in the text description and the text description is inconsistent with picture content are effectively reduced, and the accuracy of the text description generated through image-text conversion is improved.

The present application provides a text error correction method and related equipment, including: obtaining a text to be corrected; segmenting the text to be corrected by a word segmentation algorithm to obtain a word segmentation error correction text; performing error detection on the text to be corrected and the word segmentation error correction text respectively to obtain an error candidate set, wherein the error candidate set includes error words; determining a replacement candidate set based on the error candidate set; grouping the error candidate set to obtain multiple error candidate subsets; generating multiple candidate paths based on multiple error candidate subsets; determining a candidate recommendation set based on the replacement candidate set and the candidate path; and obtaining a recommended text based on the recommended candidate set by a preset algorithm. The text error correction method provided by the present application can cover all error correction paths and candidate possibilities, thereby improving the accuracy of text error correction.

The invention provides a real-time dynamic display transcription method and system, electronic equipment and a storage medium. The method comprises the following steps: segmenting a single-channel voice stream into continuous multi-frame voice streams; performing human voice detection on the voice stream to obtain a detection result; if a preset number of continuous target voice streams exist, streaming transcription is carried out on each frame of target voice stream and the acquired human voice stream in real time by using a current language through a streaming transcription model; in the streaming transcription process, accumulated audios are collected every preset time period, and the streaming text in the preset time period is corrected by utilizing an accumulated text obtained by transcription of the accumulated audios until a human voice ending signal is detected; correcting the streaming text in the target time period by using a target accumulated text obtained by transferring the target accumulated audio in the target time period to obtain a target corrected text; decoding the target correction text to obtain and display a final text; and updating the current language by using a language obtained by performing language identification on the target accumulated audio.

The present application provides a method, device, equipment and storage medium for correcting text classification labels. The method includes: calculating the first classification support rate of the text to be corrected in each set classification category based on the classification results of the text to be corrected in each trained text classifier; predicting the second classification support rate of the text to be corrected in each classification category based on the association frequency of the associated vocabulary set constructed in each classification category in the text to be corrected; and correcting the marked classification labels of the text to be corrected using the first classification support rate and the second classification support rate of the text to be corrected in each classification category. The present application analyzes the actual classification of the text to be corrected from the two perspectives of the text classifier and the associated vocabulary set, realizes the correction of the text classification label, ensures the accuracy of the text classification label, and subsequently uses such corrected text to train the text classification model to improve the classification accuracy of the text classification model.

The invention discloses a model-based text processing method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a training text, and obtaining a reference text and reference phonetic notation of the training text, the reference text being obtained by carrying out text error correction processing on the training text, the reference phonetic notation is obtained by carrying out phonetic notation processing on the reference text; calling a task model to carry out text error correction processing on the training text to obtain a prediction error correction text of the training text; calling the task model to carry out phonetic notation prediction processing on the training text to obtain predicted phonetic notation of the training text; on the basis of the difference between the reference text and the predicted error correction text and the difference between the reference phonetic notation and the predicted phonetic notation, a task model is optimized, and the optimized task model is used for processing a text error correction task. By adopting the method and the device, the text error correction accuracy can be improved and the text error correction effect can be optimized in the text error correction task.

The embodiment of the present application provides a display device, a text correction method and a server, wherein the display device includes a display and a controller, wherein the controller is configured to: in response to receiving a voice command input by a user, perform voice conversion on the voice command to obtain a text to be corrected; control the display to display the text to be corrected; correct the text to be corrected based on a similar sound-shape confusion set and a graph attention mechanism to obtain an initial correction text, perform candidate recall on the text to be corrected and the initial correction text, and obtain a final correction text based on the sorting results of the recalled texts; and control the display to refresh the text to be corrected to the final correction text. The embodiment of the present application generates a pronunciation-similar knowledge graph and a shape-similar knowledge graph based on the confusion set corresponding to the text to be corrected, integrates the pinyin and glyph-related knowledge of Chinese characters into a graph neural network, extracts deep semantic information between similar characters, and can effectively utilize the knowledge of similar sound and shape to improve the accuracy and recall rate of error detection and correction.

Embodiments of the present specification provide a text processing method, a text processing model training method, an article correction method, a computing device, a storage medium and a computer program product wherein the text processing method comprises: determining a to-be-processed text, and determining a reference correction text associated with the to-be-processed text, the confidence coefficient of the reference correction text is greater than that of the to-be-processed text; inputting the to-be-processed text and the reference correction text into a text processing model to obtain a target text corresponding to the to-be-processed text; wherein the text processing model is obtained by training according to a label text, a label correction text associated with the label text and a sample text, and the sample text is constructed according to the label text and the label correction text. The text processing model can learn more errors possibly occurring in the text, so that the error correction capability of the text processing model is improved, and the processing performance of the text processing model is ensured.

The present disclosure relates to the field of natural language processing, and specifically to a medical text error correction method, a medical text error correction device, a storage medium, and an electronic device. The text error correction method comprises: obtaining a medical text to be corrected input by a user; dividing the medical text to be corrected into short sentences to obtain a plurality of short sentences; using a vocabulary typo correction model to perform vocabulary typos recognition and correction processing on each short sentence in the medical text to be corrected to generate a first error correction text; using a semantic typo correction model to perform semantic typos recognition and correction processing on the first error correction text to generate a second error correction text. The present disclosure can improve the accuracy of medical text error correction.

The invention relates to a multi-stage spelling error correction method and device for a medical image report and a medium, in a non-term error correction stage, for each text segment, a pinyin error correction knowledge base and a polyphone probability base are called, screening and replacement are carried out according to text similarity and editing distance, candidate correction for general spelling errors is generated, and the spelling error correction efficiency is improved. Obtaining a text fragment after non-term error correction; in the term error correction stage, a medical term package is called, candidate correction for term spelling errors is generated and screened through candidate interval positioning, set similarity screening and sequence similarity calculation, and text segments after term error correction are obtained; and enabling the text segments after non-term error correction and the text segments after term error correction to correspond to each other at the text position, correcting the candidates with overlapping or conflicting, and performing resolution and combination according to a preset priority rule to obtain a final error correction result. Compared with the prior art, the method has the advantages of wide range, high accuracy, high reliability and the like.

The invention discloses a real-time discourse correction method, a related device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current recognition text of a real-time audio stream, and obtaining a plurality of historical recognition texts subjected to text correction before the current recognition text and historical correction texts obtained through text correction; based on at least one of the current recognition text and the historical correction text, retrieving in the scene knowledge graph to obtain a reference knowledge text related to the execution of the text correction; wherein the scene knowledge graph is a reference knowledge graph related to an interaction scene where the real-time audio stream is located; and performing text correction on the current recognition text based on the historical correction text and the reference knowledge text to obtain a current correction text of the current recognition text. According to the scheme, the accuracy of real-time discourse correction can be improved, especially when the real-time discourse relates to terminologies or specific domain knowledge.

The invention provides a speech recognition error correction method and device, and relates to the technical field of data processing, and the method comprises the steps: inputting a current audio frame in a to-be-recognized user audio into a speech recognition model, and obtaining the acoustic features and first text characters of the current audio frame; inputting the acoustic feature and the first text character of the current audio frame and the historical correction text sequence into a speech recognition error correction model to obtain a correction text character corresponding to the current audio frame; wherein the historical corrected text sequence is a corrected text sequence obtained after historical audio frames, before the current audio frame, in the audio of the user to be recognized sequentially pass through a voice recognition model and a voice recognition error correction model; the speech recognition error correction model is obtained by training based on a training sample carrying a forced alignment text label, and the forced alignment text label is obtained by performing forced alignment on a real text label corresponding to the training sample and a speech sample signal corresponding to the training sample.

This invention relates to the field of data processing technology and discloses a multilingual voice data processing method for real-time intercom communication of VHF equipment at sea. This method addresses the problem that during voice data processing, intercom content may be presented in a simplified manner rather than with complete semantics. The method includes: determining the context dependency of the current intercom round corresponding to the correction text; when the correction text is determined to have a context dependency, determining semantic missingness to obtain a semantic missingness determination result; when the semantic missingness determination result indicates that the correction text has semantic missingness, extracting context text associated with the current intercom round from historical intercom text, and completing the missing communication object, location description, risk background, or operation object in the correction text based on the context text to obtain the final completed text. This effectively improves the accuracy, continuity, and practical reliability of multilingual voice data processing at sea.

The application belongs to the technical field of natural language processing, and particularly relates to a disorderly text correction method based on a pointer network; the method comprises the following steps: obtaining correction text data and constructing labels; extracting a word-level feature vector of the correction text data; extracting a local feature vector according to the word-level feature vector of the correction text; splicing the word-level feature vector and the local feature vector to construct a fusion feature vector; constructing a span pointer score table and a next pointer score table by using a query mechanism according to the fusion feature vector; narrowing the next pointer score table according to the span pointer score table; reordering the disorderly text according to the span score table and the next pointer score table to obtain a final corrected text; calculating a total loss of a model and adjusting model parameters according to the total loss to obtain a trained disorderly text correction model; and using the trained model to correct the text; the application improves the accuracy of an automatic proofreading system and the model reasoning speed is faster.

An embodiment of the present application provides a generative summary error correction method for fact consistency. The technical key points are as follows: a retrieval module and an error correction module are established. After preprocessing the summary to be error corrected and the corresponding source text, they are input into the retrieval module in pairs. The retrieval module, in the form of a three-classification task, retrieves evidence information from the corresponding source text to match each sentence content of the summary to be error corrected to obtain a matching set; the error correction module decodes the masked features through the encoder, masker, and decoder to generate an error correction text; the error correction text is evaluated from two aspects of content and factuality. After calculating the benefit of the error correction text, a record is generated and added to the sample pool; N records with the largest sample benefits are selected from the sample pool to calculate the policy gradient loss to guide the parameter learning of the error correction module. The learned retrieval module and error correction module are used to correct the summary to be error corrected in the target data.

The invention discloses a text processing method, a related device, computer equipment and a storage medium. The method provided by the invention comprises the steps of obtaining a global correction text corresponding to a to-be-processed text, wherein the global correction text is obtained after correction; acquiring a local understanding text corresponding to the to-be-processed text; constructing a first prompt text according to the global correction text, the local understanding text and a first prompt word template, wherein the first prompt word template is used for guiding a text correction model to correct the text; on the basis of the first prompt text, a local correction text for the local understanding text is obtained through a text correction model, and the local correction text is obtained after the text correction model corrects the local understanding text on the basis of the global correction text. According to the method and the device, the text understanding accuracy of the model can be improved, and the local understanding text does not need to be manually corrected, so that the labor cost is saved, and the text correction efficiency is improved.

The invention discloses a text error correction method and device, a model training method and device, equipment and a storage medium, and belongs to the technical field of electronic equipment. The text error correction method comprises the following steps: performing voice recognition on received first voice data to obtain a first text; a first error correction text of the first text is obtained on the basis of first information and the first text, the first information is visual information obtained on the basis of second information, and the second information comprises at least one of the following items: content displayed by a display screen when first voice data is received; receiving an image collected by a camera when the first voice data is received; and outputting reply information corresponding to the first voice data based on the first error correction text.

The present disclosure relates to a method for text error correction, an apparatus, a computer program product, a non-transitory computer readable storage medium, a method for automatic speech recognition (ASR) using the text error correction method, and a method for training a neural network model for text error correction. The method for text error correction comprises the following steps: generating an error correction text and an error correction label of a to-be-processed text based on the to-be-processed text by using a first neural network model, wherein the error correction label indicates whether the error correction text is improved relative to the to-be-processed text; and selecting a method for outputting the error correction text or the to-be-processed text as a correction result based on the error correction label. The method and the device can avoid the situation that the error is more and more corrected under the scene of correcting the error of the text, and improve the error correction effect of the text.

The present application provides a text error correction method and device, which can be applied to the fields of artificial intelligence (AI) and optical character recognition (OCR). The method includes: obtaining a first optical character recognition (OCR) text and a first error correction text, wherein the first error correction text is obtained by correcting the first OCR text using an error correction model; aligning the first OCR text and the first error correction text to obtain a second OCR text and a second error correction text, wherein the second OCR text and the second error correction text have the same number of words; determining a text error correction position for the second OCR text and the second error correction text; and determining the optimal error correction word at the error correction position among the second OCR text, the second error correction text, and the candidate words. The method of the present application can improve the accuracy of text error correction.

The application provides a kind of picture-text conversion correction method, equipment and storage medium, it is related to image processing field.The method comprises: obtaining the original image to be processed and inputting original image into the preset graph text model to generate text description;Based on original image, text description and preset text graph model, obtain simulated image;Original image and simulated image are analyzed to obtain image slice pair and the similarity data of each image slice pair;According to similarity data, the image slice pair is analyzed to generate correction text;The correction text is corrected to obtain target text by text description.Through the way of original image generating text description, text description generating simulated image, original image and simulated image are compared to correct text description, effectively reduce the object illusion in text description and the situation that text description and picture content are inconsistent, improve the accuracy of text description generated by picture-text conversion.

The embodiment of the present application provides a text error correction method and a text error correction device, an electronic device and a medium, which belong to the field of financial technology. The method includes: inputting the target text into a text error detection model for text error detection to obtain text marking data, the text marking data is used to mark the correct characters and incorrect characters in the text characters; inputting the target text into the text error correction model based on the text marking data; extracting the text phonetic features of the text characters based on the phonetic feature extraction submodel; extracting the text glyph features based on the glyph feature extraction submodel, the text glyph features include the first mask features of the incorrect characters; extracting the text semantic features based on the semantic feature extraction submodel, the text glyph features include the second mask features of the incorrect characters; performing text prediction on the text phonetic features, text glyph features and text semantic features based on the error correction submodel to obtain the target error correction text. The embodiment of the present application can improve the accuracy of text error correction.

To assist communication between speakers.SOLUTION: A processing device 10B includes: a voice recognition unit 12 for converting voice data which a first user and a second user emit into a text; and a correction unit 17B which corrects the text converted by the voice recognition unit 12 by using a machine learning model trained to correct the text on the basis of the text obtained by converting voice data which a speaker emits and data based on an image obtained by imaging the speaker, and outputs a corrected first correction text to a user interface which the first user and the second user use.SELECTED DRAWING: Figure 18