104 results about "Grammatical error" patented technology

The invention provides a robot control method and system based on a big language model, a double big language model evaluation mechanism is adopted, a big language model of a code generation and modification module uses Claide 3.5 sonnet, control codes are generated by the Claide 3.5 sonnet, evaluation is provided by adopting a gpt-4o evaluation model of OpenAI to optimize suggestions, and the big language model of the code generation and modification module is used for evaluating the control codes. The risk that problems are difficult to find due to homogenization of the same module is reduced, and the generation quality, safety and supportability are improved; in addition to a double large language model evaluation mechanism, due to the fact that safety prompt words are added into prompt words of a generation model and a generation template example is provided, operation of the generation model in a specific framework can be limited, the possibility of error occurrence is reduced, and specification and safety of a generation effect are further provided; in addition, the control class verification module constructs a simple simulation environment after the control class codes are generated, so that grammar errors and logic vulnerabilities of the generated codes can be screened in advance, and potential safety hazards are further reduced.

The invention relates to the technical field of online learning, in particular to an interaction-based mobile English learning system, which comprises a grammar error interaction recognition module, an error item pushing frequency regulation and control module, a learning load dynamic monitoring module, a learning difficulty dynamic adjustment module and an English skill interaction aggregation module. According to the method, the error event density change in the English learning interaction information of the user is dynamically monitored, the error event time sequence is established by using the error labeling field, the error type classification and the timestamp, and the learning content pushing frequency adjustment object is accurately determined according to the error density change amplitude, so that efficient error item accurate intensified training is realized; meanwhile, according to the synchronous change amplitude of the task response time increase rate and the answer accuracy change rate, the change condition of the learning load of the user is recognized in real time, the difficulty level of the learning content is dynamically and automatically adapted, dynamic regulation and control between the learning difficulty and the learning state of the user are formed, and the learning acceptability and effect of the user are improved.

The invention relates to the field of wisdom education, and particularly discloses a teaching strategy optimization model and grammar error early warning method based on data mining, and the method comprises the following steps: S1, obtaining original code data in a student code library in real time, and preprocessing the original code data to obtain a structured data matrix; s2, for the structured data matrix, generating a grammar parse tree through a grammar parser, positioning error nodes and extracting context features by using a node traversal algorithm, and constructing a single-error multi-dimensional feature vector; meanwhile, an error association rule base is constructed based on historical error data, a causal relationship across error types is identified, and dynamic knowledge graph data is formed. According to the technical scheme, association analysis and deep mining can be carried out on the multi-source learning data, dynamic solution suggestions for student individual errors or group generality errors are formed, and teachers are assisted to quickly adapt to dynamically changing learning requirements of the students.

The invention discloses an English writing grammar error detection system based on deep learning. The system comprises an original data acquisition module, a data preliminary processing module, a grammar error positioning model construction module, a grammar error classification model construction module and a grammar error detection module. The invention relates to the technical field of text grammar error detection, in particular to an English writing grammar error detection system based on deep learning. A data preliminary processing method of text data trimming, text vectorization, data standardization and data set segmentation is adopted; a lightweight gradient elevator model is adopted as a grammar error positioning model, and the error positioning precision is improved through adaptive fusion of rule conflicts and statistical mode anomalies; a transformer model is adopted as a grammar error classification model, and classification accuracy is improved by modeling dependency relationships among words and introducing graph structure constraints.

The invention provides an execution feedback and iterative optimization-based natural language to SQL (Structured Query Language) conversion method. The method comprises the following steps of: generating a plurality of candidate SQL statements; performing parallel execution on the candidate SQL statements, and capturing grammar error information in execution results and line number classification numbers of different candidate SQL return results; generating an error pattern abstract based on the grammar error information, converting the error pattern abstract into a natural language constraint instruction, and injecting the natural language constraint instruction into a prompt word generated in the next round; when the line number classification number is smaller than or equal to a preset threshold value, a self-consistency voting mechanism is adopted, a candidate SQL set with the largest number of returned same lines is selected, and the SQL with the shortest execution time is selected from the set to serve as final output. According to the method, the accuracy, robustness and execution efficiency of conversion from the natural language to the SQL can be remarkably improved by executing a feedback-driven multi-round iterative optimization mechanism.

The invention provides a personalized intelligent evaluation system and method based on a multi-role intelligent agent, and belongs to the technical field of intelligent evaluation in the education field, and the system comprises a data input module which is used for collecting student homework data, personal learning information and evaluation standards; the personal learning information base module is used for storing personal learning information of students and comprises a learning condition analysis unit, a learning style and preference unit, a learning demand and target unit and a teacher marking information unit; the evaluation standard library module is used for storing evaluation standards and evaluation cases preset by teachers; by combining the pre-training language model, sentiment analysis, supervised learning and deep learning algorithms, the homework content of students can be deeply understood and analyzed, grammar errors, logic problems and innovation points can be automatically identified, the language style of teachers can be understood through sentiment color analysis, and personalized modification suggestions can be provided for the students.

The invention discloses an intelligent error correction and style optimization system and method for business English writing, and relates to the technical field of natural language processing and artificial intelligence, and the system comprises the following components: a user interaction module, a data storage module, a language ability evaluation module and a self-adaptive error correction feedback and style optimization module. Through a multi-dimensional evaluation algorithm based on dynamic weight adjustment, historical writing data of a user is deeply analyzed, an evaluation system including four dimensions of grammar errors, vocabulary application, sentence structures and business profession is constructed, the system comprehensively evaluates the language ability of the user, and the user experience is improved. And the evaluation weight is dynamically adjusted according to the capability improvement rates of the users in different dimensions, so that the language capability levels of the users are more accurately divided, and the system can provide personalized error correction feedback and style optimization suggestions for the users with different language levels based on the evaluation result.

The invention discloses a grammar error processing method and device, a cluster, a storage medium and a program product, and relates to the technical field of computers. By adding the processing flow after detecting that the grammar error exists in the database statement, the robustness and compatibility when the computing device analyzes the database statement are improved, a user does not need to modify a service code or completely shield a keyword in advance, so that the use threshold is reduced while the function of the database is ensured to be fully reserved. The method comprises the steps that after a first database statement is received, a target grammar unit in the first database statement is determined, when the target grammar unit is used as a keyword for grammar analysis, the target grammar unit does not conform to a first grammar rule, and the target grammar unit comprises one or more characters; and processing the target grammar unit, wherein the processed target grammar unit conforms to at least one grammar rule including the first grammar rule during subsequent grammar analysis.

The invention discloses a text error correction method and device based on GRU-Transform, electronic equipment and a storage medium, and belongs to the technical field of text processing. The method comprises the following steps: acquiring a plurality of data sets, wherein the data sets comprise a spelling error data set, a grammar error data set, a punctuation error data set and a similar word error data set; constructing a text error correction data set based on the plurality of data sets; a text error correction model is trained based on the text error correction data set, the text error correction model comprises a feature extraction sub-model, a feature fusion sub-model, a coding-decoding sub-model and a loss calculation sub-model, and the coding-decoding sub-model is a GRU-Transform model; and inputting the to-be-corrected text data into the trained text error correction model to obtain an error-corrected text output result, thereby improving the accuracy and applicability of text error correction.

The invention discloses a database query system and method for converting a natural language into an SQL (Structured Query Language) statement based on a Transform architecture, and the system captures a long-distance dependency relationship and complex semantic information in the natural language based on the Transform architecture, so that the semantic accuracy is improved, the wrong and repeated operation of generating the SQL statement is reduced, and the data acquisition efficiency is improved. On the other hand, the problems of grammar errors, semantic inconsistency and the like are verified, recognized and corrected through the SQL error correction module, database query failures are reduced, the user operation experience is improved, meanwhile, the error correction module can conduct compensation through reverse verification and correction, the dependence of the system on the precision of a single module is reduced, and the overall stability is improved; and for similar related problems, SQL statements can be directly extracted from the mapping table, so that time-consuming processing links such as Transform semantic analysis and SQL generation are omitted, and the waiting time and the computing power consumption of a user are remarkably reduced.

The invention provides a large language model and semantic feedback iterative optimization process control program generation method, and belongs to the technical field of automatic control. And generating a preference data set through a compiler and semantic expert feedback, and performing model optimization by using the data set. The training bottleneck of a traditional data-driven model is avoided, and the generation capability of the model can be continuously optimized through dynamic adjustment and feedback under the condition of data scarcity; a dynamic iterative optimization mechanism is constructed by introducing the feedback of a compiler and a semantic expert, and the generated process control program can be checked and corrected in real time. The compiler feeds back to ensure the grammar correctness of the generated program, and a semantic expert evaluates from the aspects of logic and task intention. The problems of grammar errors and semantic mismatching in a traditional method are avoided. According to the method, the compiling passing rate and semantic accuracy of the process control program are effectively improved, and the quality and adaptability of the generated process control program are remarkably optimized.

Implementations described herein relate to utilizing a large language model (LLM) to determine whether a natural language (NL) based input is grammatically incorrect and notifying a user based on the determination. A structured LLM query may be generated, based on the NL based input, that includes an LLM prompt to cause the LLM to generate an LLM response including an indication of whether the NL based input is grammatically incorrect. An LLM response may be generated, based on causing the structured LLM query to be processed using the LLM, that includes the indication of whether the NL based input is grammatically incorrect. Responsive to determining that the NL based input is grammatically incorrect based on the LLM response, a feedback output may be caused to be rendered at the client device, or an additional client device, that indicates the NL based input is grammatically incorrect.

The invention belongs to the technical field of language learning, and discloses a language training method and device, equipment and a medium, and the method comprises the steps: constructing a candidate training unit pool; calculating a push priority score of each training unit in the candidate training unit pool; sorting all the training units in the candidate training unit pool from large to small according to the push priority scores, and selecting a preset number of training units; aiming at each selected training unit, adopting an interference strategy which corresponds to the training unit and contains grammar errors and / or cultural conflicts, generating training questions corresponding to the training unit based on a large language model, and forming a training question set by all the training questions; and performing language training according to the training question set. And the language anti-interference capability, the cross-culture sensitivity and the deep grammar grammar of the user can be effectively improved.

The embodiment of the invention provides a model training method, a text error correction method and a related product, and relates to the technical field of machine learning. According to the model training method provided by the invention, by introducing the model training data including various error types such as spelling errors, grammar errors, logic errors and format errors, various errors possibly existing in the to-be-corrected text can be comprehensively covered and recognized. Moreover, the word segmentation result of the text data is subjected to masking processing through a first sub-model of the machine learning model, the word segmentation result of the text data is subjected to disordering processing through a second sub-model, and the machine learning model is trained according to the annotation data, the result after masking processing and the result after disordering processing; the recognition capability of the machine learning model for the error mode is enhanced, the learning capability of the machine learning model for the correct text structure is also promoted, and the text error correction model shows higher accuracy during text error correction.

The invention relates to a Vietnamese grammar error correction corpus construction method based on error type perception of a large model, and belongs to the field of natural language processing. According to the method, firstly, a voice recognition model is used for simulating Vietnamese grammar errors in a real scene, a preliminary error correction data set is generated, then through deep analysis of distribution rules and grammar structure features of typical errors in the data set, a chain thinking cue (CoT) mechanism fusing error type features is designed in a targeted mode, and the error type features of the Vietnamese grammar errors are extracted. Guiding a large language model (LLM) to generate synthetic statements containing predetermined grammar errors in batches; thirdly, in order to enhance corpus quality, synchronously implementing a web crawler to collect a native Vietnamese text, and constructing a pure monolingual corpus through multi-layer filtering and cleaning; and finally, the generated synthetic data needs to be strictly verified and processed to ensure that the error type is consistent with a preset target, and a pre-training model normal form and a large model normal form are strengthened in a two-stage fine tuning manner, so that the generalization ability of a grammar error correction model is effectively improved, and the problem that Vietnamese grammar error correction corpus is deficient is solved.

The invention relates to a large model corpus processing method and system. The method comprises the following steps: performing data acquisition on language data; value data identification is carried out on the collected data, and a valuable area is reserved; extracting the reserved area in the form of a paragraph document; the paragraph document is converted into a unified text format, and invalid content deletion, format and grammar error correction, content value screening and format unified processing are carried out; performing duplicate removal processing on the data; carrying out overall quality inspection on the data subjected to the duplicate removal processing so as to optimize the processing process; performing feature extraction on the data through a deep learning model to obtain corpus features; according to corpus features, semantic information is extracted through a semantic understanding model and used for training and application of a large model; and optimizing the data processing process according to the training and application results and feedback information of the large model. Compared with the prior art, the method has the advantages that unified processing of various large model training data is realized, and the accuracy and efficiency of data processing are improved.

The invention relates to a grammar error correction method and system based on continuous knowledge distillation, and belongs to the field of natural language processing. The method comprises the following steps: taking a distillation knowledge set as input of a teacher model and a student model, performing quantitative scoring on the basis of output information entropy, and dividing the distillation knowledge set into a positive set and a negative set; a distribution-based distillation strategy is constructed, so that the student model learns from the teacher model on the positive set, the student model on the negative set avoids the same error as the teacher model, and knowledge distillation is realized; constructing a knowledge inheritance strategy to realize continuous learning of a student model; total training loss is generated through knowledge distillation, student model continuous learning and student model training loss; and calculating a loss function gradient according to the training total loss, and updating a model parameter weight according to the gradient and a learning rate to obtain a final grammar error correction model. According to the method, the problems of low model training performance and lack of parallel corpora are solved, efficient knowledge transfer is realized, and the grammar error correction model effect is improved.

The invention relates to a handwritten mathematical expression recognition and comparison method based on large model reasoning. The method comprises the following steps: pre-training to obtain an OCR (Optical Character Recognition) model of a handwritten mathematical expression; based on an output result of the OCR model, combining input data of the OCR model to perform fine tuning on a large language model LLM; and inputting a latex text sequence corresponding to the standard answer into the fine-tuned large language model, and outputting to obtain a recognition comparison result. Compared with the prior art, the method has the advantages that the problems of grammar errors of predicting latex character sequences and ambiguity of texts to be recognized can be reduced, the recognition accuracy of handwritten mathematical expressions can be improved, and meanwhile, based on the reasoning ability of a large model, the problem of heterogeneous answer comparison when standard answers are not expressed uniquely can be solved.

The invention provides a man-machine collaborative program repairing method based on a large language model, relates to the technical field of automatic program repairing, and remarkably improves the accuracy and success rate of program repairing. According to the method, a large language model is guided through preliminary analysis of manual teaching, errors in a program can be more accurately positioned and repaired by combining a repairing process of multiple iterations and more information introduced step by step, misjudgment and omission are reduced, and the program repairing efficiency is greatly improved. Compared with a traditional method, blind exploration of a large language model on complex problems is avoided through a man-machine cooperation mode, time and computing resources are saved, and adaptability of program repair is enhanced. According to the method, program defects of various types and complexity can be handled, and simple grammar errors or complex logic errors can be effectively handled through a step-by-step deep repair process.

The embodiment of the invention relates to the technical field of information, in particular to an SQL resource estimation method, device and equipment based on a large model, and the method comprises the steps: receiving an SQL statement input by a user, correcting a grammar error, and outputting a correct SQL code; analyzing fields, functions and limiting conditions in the SQL codes by configuring a large language model of a Python code executor tool; python codes are automatically written according to fields and limiting conditions, an operation environment is configured and executed, the size of a source data table is read, data are screened according to the limiting conditions, and the size of a screening table is obtained; according to the static parameters of the function, retrieving the original knowledge content to obtain an original knowledge set containing the Spark principle and historical execution data; and estimating resource parameters based on the SQL static parameters, the double-table dimension information and the original knowledge set. Spark parameters are generated through principle calculation and historical verification, and accurate resource configuration, cluster management and control and multi-field application requirements are met.

The invention discloses a language learning evaluation method based on a deep learning algorithm, and relates to the technical field of language processing, and the method comprises the steps: receiving a voice signal and original text content, constructing a noisy voice sample through an adversarial disturbance generator, inputting a pre-trained voice recognition model, and outputting a voice recognition text and an anti-noise confidence score; constructing a context semantic graph in the semantic analysis path, generating an edge attention weight, calculating a semantic coherence index, and matching a predefined grammar rule base in the activated grammar analysis path to generate a grammar error tag; and dynamically generating feedback content based on the multi-dimensional evaluation matrix, and updating a preset corresponding threshold value and a rule base weight according to the adoption of the user on the feedback content. According to the method, collaborative quantitative analysis of pronunciation, semantics and grammar problems is realized by constructing a multi-dimensional evaluation matrix of an anti-noise confidence score, a semantic coherence index and a grammar error tag.

The invention provides an AI-based multi-user interactive language learning system and method, and relates to the technical field of interactive language learning. Real-time structured analysis of a multi-user mixed voice stream is realized through a voiceprint separation technology, a grammar error density spectrum is constructed to quantify user grammar error distribution characteristics, and a culture conflict intensity field is established; cross-cultural dialogue conflict intensity is dynamically calibrated, a semantic transition trajectory is generated, a logic fault evolution law is accurately captured, and a traditional single-dimensional static learning mode is broken through; a language adaptation index is dynamically generated on the basis of a weight prediction model, collaborative modulation of a grammar reconstruction task package, a culture tuning script and a semantic bridging task is achieved, culture conflict mediation deeply conforms to real-time conflict intensity, and dialogue fault features are accurately matched through semantic logic repair; through a three-channel task distribution mechanism of directional insertion, multicast broadcast and random allocation, efficient collaboration of personalized grammar training, immersive culture drill and distributed logic repair is ensured.

The invention belongs to the technical field of English teaching methods, and particularly relates to an intelligent English teaching method and system and a storage medium, and the method comprises the steps: constructing a multi-dimensional linguistic feature analysis model, and extracting lexical features, syntactic relationship features and semantic deviation features in a text input by a student through a natural language processing technology; dynamic student portraits are established based on the cognitive psychology theory, cognitive level labels are updated according to real-time learning data, and the data comprise grammar error clustering distribution, spoken language fluency indexes and vocabulary association response time; and generating a personalized teaching path, matching teaching materials from the hierarchical resource library according to the cognitive level label, and dynamically adjusting the complexity and presentation form of a teaching strategy. Lexical, syntactic and semantic deviation features are extracted through a natural language processing technology, student error types can be accurately positioned, the problem that traditional error correction only stays on surface modification is avoided, and cognitive tags are updated based on real-time learning data.

The invention provides an English text grammar error proofreading method paying attention to previous text features, and the method is a verification method composed of a sentence part-of-speech vectorization module, a sentence information feature extraction module, a previous text-source sentence information feature fusion module and a proofreading result screening generation module. According to the method, cross-sentence text modeling and a fine-grained error type recognition mechanism are creatively fused, and the problems that the recognition rate of grammar errors caused by cross sentences in a long text is low, and the capability of distinguishing diversified error types is insufficient are effectively solved.

The invention relates to an SQL statement generation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing entity identification, table label identification and knowledge enhancement on a question, further supplementing business knowledge for the question, and realizing accurate understanding of question semantics, accurate matching with a database table and avoidance of grammar errors in the SQL statement generation process by combining characteristic knowledge and metadata corresponding to a business field. The accuracy of the SQL statement generated through the large language model and used for solving the problem is improved, and the generation efficiency is improved.

The invention discloses a grammar error correction method based on large model syntactic preference optimization. According to the grammar error correction method, syntactic relevance between retrieval example sentence pairs and syntactic relevance between the retrieval example sentence pairs are cooperatively utilized. On one hand, a Monte Carlo tree search algorithm is used for exploring the influence of syntactic structure differences among retrieval examples on error correction performance, search path selection is adjusted based on dynamic weights, and syntactic perception corpora containing hidden syntactic association are constructed. On the other hand, by constructing a syntactic preference alignment mechanism, syntactic knowledge contained in syntactic perception corpora is effectively utilized, dynamic adjustment of grammar error correction model parameters is achieved, and then the performance of the grammar error correction model in a complex grammar error correction task is improved. According to the method, a comprehensive contrast experiment is carried out on two Chinese error correction data sets and two English error correction data sets. Experimental results show that the method can effectively integrate the structured syntactic features in the retrieval examples, and effectively relieve the over-correction phenomenon of the grammar error correction model.

The invention discloses an entry translation method and device, a medium and equipment. The method comprises the steps that firstly, an Android source entry file is obtained and analyzed, all key value pairs in the Android source entry file are extracted, and the key value pairs are basic data of entry translation; then, the extracted key value pairs are translated through a preset external translation interface, and high efficiency and accuracy of translation are ensured. And after translation is completed, a target language resource file conforming to an Android internationalized directory structure is generated according to a translation result, and the structure enables the application to support multiple languages. Finally, format verification and grammar error correction processing are carried out on the generated target language resource file, the format verification comprises checking whether the initials of the English entries are capitalized or not, and the initials are converted into capitalized forms to conform to English grammar specifications; the grammar error correction relates to escape processing of placeholders and special characters so as to ensure the grammar correctness and compiling stability of resource files. And finally obtaining a high-quality source entry file translation result.

The present invention discloses a text processing method and device. The method comprises: extracting erroneous words from text information, wherein the erroneous words include at least one of the following types: grammatical errors, vocabulary errors, and spelling errors; obtaining correction words corresponding to the erroneous words based on word collocation information of the field to which the text information belongs, wherein the word collocation information is obtained based on the corpus of the field to which the text information belongs; and correcting the text information based on the correction words to obtain the corrected text information. The present invention solves the technical problem in the prior art that the accuracy of the trial record obtained by voice conversion of the recording during the trial is low.

In an embodiment, the disclosed technologies are capable of detecting a tone in text. A detected tone may be used to inform a decision made by and / or output produced by a grammatical error correction system. A set of candidate tones may be presented to a user for feedback. User feedback on the candidate tones may be used to improve subsequent tone detections.

A method for natural language to SQL (NL2SQL) statements driven by a hybrid approach combining templates and a large language model includes the following steps: constructing a pre-built SQL template library to assist the large language model in quickly matching syntactically correct SQL template skeletons; constructing a database schema information knowledge base to assist the large language model in accurately extracting entities; constructing a large-model natural language parsing and entity extraction engine to extract entity information; constructing a large-model-based template matching engine to match corresponding target SQL templates from the template library using the large language model; and constructing a large-model-based template filling engine to fill the extracted entity information into the corresponding placeholders of the selected target SQL template, generating the final executable SQL statement. By using pre-built SQL templates to ensure syntactic correctness and utilizing the large language model to parse natural language and extract entities to ensure semantic flexibility, this method solves the syntax errors and illusion problems that may occur when generating SQL using pure LLM, significantly improving the practicality and accuracy of NL2SQL technology.