80 results about "Grammatical error" patented technology

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 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 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 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 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 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.

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.

Existing code generation models rely on learning the semantic association between natural language descriptions and codes to generate code, and lack explicit constraints on the grammatical structure of the code, which may lead to grammatical errors in the generated code. In order to solve the problem of grammatical errors in the code, some methods perform grammatical corrections on the generated code through post-processing, but post-processing is an independent step, separated from the code generation model itself, and cannot directly affect the generation ability of the model during the code generation process. In addition, most post-processing methods are based on static rules, which may be difficult to adapt to complex code structures or flexible grammars. In this regard, the present application discloses a code generation method and related equipment based on a grammar discriminator, which includes: inputting the generation description information corresponding to the code to be generated into the code generation model, and obtaining the target code output by the code generation model. In the present application, grammatically accurate code is generated by the code grammar optimization function of the code generation model.

The present invention discloses a method for fine-grained error detection and repair of NC code based on 2-gram and mutual information model. The method is as follows: Step 1: A local operator identifies key instructions and potential abnormal feature areas by analyzing the functional instructions and trajectory data of the NC code; Step 2: A preliminary check of the instructions is performed using lexical, grammatical, and logical detection techniques to identify possible lexical and grammatical errors; Step 3: If no errors are detected, the relationship between the instructions is deeply analyzed using 2-gram and mutual information model to find possible abnormal instruction combinations; Step 4: For abnormal points in the trajectory data, interpolation technology is used to repair the abnormal points to ensure that the code passes integrity detection. By analyzing the functional instructions and trajectory data of the NC code and combining lexical, grammatical, and logical detection techniques, this method can accurately locate and repair NC code anomalies, thereby improving the safety and efficiency of the production process.

A large model-based text error correction method and apparatus, a device, and a storage medium, relating to the technical field of artificial intelligence. The method comprises: on the basis of a large language model, generating first training data according to a first preset instruction, and manually annotating preset data to generate second training data, so as to construct a training data set; constructing a corresponding target data set by means of second preset instructions corresponding to a preset text error correction task and the training data set, so as to use the target data set to fine-tune the large language model to obtain a target large language model; and acquiring a text to be error-corrected, and determining error correction instructions corresponding to said text, so as to concatenate said text and the error correction instructions and then input the concatenated result into the target large language model to obtain an error-corrected target text. The data generated on the basis of the large language model is mixed with the manually annotated data, and a large model is fine-tuned with different instructions for different tasks, so that spelling error correction and grammatical error correction are chained in series, thereby effectively improving the accuracy of text error correction.

The invention discloses a Chinese grammar error correction multi-task training method based on thinking process distillation, which comprises the following steps: in a data construction stage, guiding a DeepSeek model by utilizing a designed constraint condition and a prompt project, and taking the DeepSeek model as a teacher model, in a training stage, taking a small-volume large language model as a student model to learn knowledge of the teacher model, and taking a small-volume large language model as a student model to learn the knowledge of the teacher model. The semantic comprehension ability of a teacher model is migrated from multiple dimensions by combining structured supervision and a multi-task learning mechanism, so that tasks are mutually enhanced, the same wrong sentence can be trained at the same time in multiple dimensions, and the model is promoted to learn more universal and more robust feature representation; according to the method, an end-to-end interpretable error correction process is realized, a user is helped to understand a model decision basis, and an error correction analysis and display process is added; the features of different dimensions are aligned in the multi-task learning process, so that the model learns more universal and more robust feature representation, and the generalization ability of the model in complex grammar error, composite error and zero sample scenes is remarkably improved.

The invention discloses an intelligent subjective question marking method and system supporting multi-text mixing, relates to the technical field of online education, effectively captures cross-paragraph information fusion and logicality through multi-granularity alignment and nonlinear fusion strategies, and greatly improves the scoring accuracy of complex answers. And in combination with Chinese and English differential quality evaluation, accurate scoring of Chinese coherence and structural integrity is realized, and grammar errors can be accurately marked and word matching can be optimized through English sentence-by-sentence fine batch. And explainable scoring evidences are output, personalized comments and optimized essays are generated in a targeted manner, and students are assisted in accurate improvement. A calibration mechanism and manual rechecking dynamic update parameters guarantee the scoring reliability, the paper marking efficiency is improved, the human deviation is reduced, and intelligent paper marking is promoted to be upgraded from pure scoring to combination of accurate evaluation and personalized guidance. The problems that an existing system is insufficient in multi-text mixed answer processing, lack of personalized comments and optimized essays and lack of English sentence-by-sentence fine batch are effectively solved.

The application discloses a knowledge graph-based English grammar error tracing recognition method, relates to the technical field of natural language processing, and solves the problems of low efficiency and low accuracy of English grammar error tracing. The method comprises the following steps: obtaining a plurality of sentences with grammar errors and defining the sentences as to-be-traced sentences; analyzing the lexical properties of the to-be-traced sentences, and recognizing the sentence structures corresponding to the to-be-traced sentences; analyzing the time clues corresponding to the to-be-traced sentences, and obtaining the tense structures of each to-be-traced sentence; identifying and tracing the errors in the sentences according to the sentence structures of the to-be-traced sentences and combining the knowledge graph; and the method divides the sentence categories of the to-be-traced sentences, then identifies the wrong words in the to-be-traced sentences based on the knowledge graphs of different sentence categories, and reversely searches the knowledge graph, so that the English grammar errors can be efficiently and accurately traced.

The invention discloses an improved Chinese text error correction method based on MACBERT and GEOROR. The improved Chinese text error correction method comprises the steps that spelling error recognition is conducted on an input text through an improved macbert model, and grammar error recognition is conducted on the input text through a selector model; conflict processing; carrying out post-processing on a model identification result; other types of errors are detected, including custom common language detection, sensitive word detection, time format detection, important character name detection, sorting detection, entity matching affiliation relation detection and paragraph or word repetition error type detection; the results are fused; and based on intra-domain rule post-processing, false alarms are reduced, and finally an error detection report is output. According to the error correction model architecture, a macbert model decoding mechanism is used for recognizing spelling errors, some new words are filtered through a post-processing means, an error correction result is finally output through a special use method, and the accuracy rate is high.

The invention provides a syntax error correction model training method and system based on an adversarial neural network, and the method comprises the steps: 1, employing a pre-training model as a generator, predicting a word with the maximum probability at each position to form a sentence, enabling the sentence to be used for the discrimination of a subsequent discriminator, and inputting the sentence to another generator for continuous generation; step 2, constructing a discriminator for discriminating whether the input sentence is generated by the generator or real data; 3, constructing a CyclGan network based on the two generators and the two discriminators, and achieving the game between the generators and the discriminators; and step 4, calculating cross entropy loss for back propagation to update neural network parameters. The method is automatically carried out, the high cost of manual labeling is avoided, meanwhile, the trained generator can be directly used as a grammar error correction model, an additional model structure does not need to be added, and the training process is simpler.