59 results about "Grammatical error" patented technology

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 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 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 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.

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 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 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 provides an intraoperative demanded instrument recognition method and system based on semantic recognition, and relates to the technical field of data processing. The method comprises the following steps: acquiring voice information of a surgeon; converting the voice information into statement information; performing preprocessing including grammar error correction on the statement information to eliminate grammar errors of the statement information; constructing a dual-task model comprising a BERT module, an NER module with a rule filter unit and an identifier identification module; training the double-task model in combination with a Bayesian algorithm improved based on a probability model so as to complete hyper-parameter optimization of the double-task model; inputting the preprocessed statement information into the trained dual-task model, and outputting a target entity identifier of the instrument required by the surgeon; and activating corresponding indication equipment according to the target entity identifier, and indicating the required instrument. The intention of a surgeon can be automatically and accurately identified, and indication of required medical instruments is completed.

An object of a system according to an embodiment is to overcome time and cost constraints and to efficiently learn English conversation.SOLUTION: A system according to an embodiment includes a smartphone application and a user. The smartphone application is equipped with a generation AI. The generated AI serves as a question of English conversation from the user or a conversation partner. The generation AI points out errors in pronunciation and grammar of the user in real time and feeds back improvement points.SELECTED DRAWING: Figure 1

The invention belongs to the field of intelligent voice evaluation, and particularly relates to an intelligent spoken foreign language test method based on BERT and an interactive attention mechanism. Scoring is carried out from multiple aspects of the spoken language pronunciation accuracy and fluency, the vocabulary accuracy and diversity, the grammar accuracy and diversity, the theme accuracy and the scene matching performance of the examinee, the comprehensive total score of the spoken language test of the examinee is obtained through calculation, and the spoken language test result of the examinee is output. Through fusion of multi-modal BERT feature extraction, mouth shape-speech time sequence alignment, interactive attention grammar analysis and deep semantic matching technologies, an automatic spoken language scoring system which covers full dimensions of pronunciation-vocabulary-grammar-theme and has a visual verification capability is constructed, and the limitation of a single modal is broken through. The robustness and accuracy of spoken language test evaluation are improved, and fine-grained positioning of pronunciation errors, voice intonation errors and grammar errors is achieved.

The present invention relates to a method for optimizing the diagnosis of defects in main power equipment by fine-tuning a pre-trained model based on ChatGPT. A corpus containing 80% correct sentences and 20% grammatically incorrect sentences generated by the ChatGPT API is constructed. Domain-specific long texts are converted into the CoLA dataset format. Adapter BERT is used as the infrastructure for fine-tuning. The base model uses bert-Chinese-base. After meeting the accuracy threshold, all layer parameters are frozen and the model is used as a feature extractor. The final vector representation is obtained by summarizing the output of the last hidden layer. A KGC model with excellent performance is selected and only its input layer is modified. A fully connected layer is introduced between the output layer of the pre-trained language model and the KGC model. The fully connected layer is trained and its parameters are updated during the KGC task iteration process. Finally, in the KGC model, the probabilities of candidate entities are calculated and ranked, and the top k entities are selected as the final answer. Experimental results show that this method effectively improves the performance of KGC in diagnosing defects in main power equipment.

The present invention provides a robot control method and system based on a large language model. This system employs a dual large language model evaluation mechanism. The large language model of the code generation and modification module uses Claude 3.5sonnet to generate control code, which is then evaluated and optimized using OpenAI's GPT-4O evaluation model. This reduces the risk of difficulty identifying problems during evaluation due to the homogeneity of the same module, thereby improving the quality, safety, and security of the generated code. In addition to the dual large language model evaluation mechanism, the system incorporates safety prompts into the prompts of the generated model and provides generation template examples. This restricts operation within a specific framework, reduces the likelihood of errors, and further ensures the standardization and security of the generated code. Furthermore, a control verification module constructs a simple simulation environment after the control code is generated, enabling early screening of the generated code for syntax errors and logical vulnerabilities, further reducing potential safety hazards.

The embodiment of the invention provides a dialogue information processing method and device, equipment and a storage medium. The method includes: in a conversational page, receiving a first set of text units from a language model, the first set of text units being included in a first portion of a response of the language model, the response being a text stream generated by the language model in a streaming manner based on cues; according to the received time sequence of each text unit in the first group of text units, presenting a current response for the cue word; and processing the current response based on syntax check data, the syntax check data indicating a syntax error in the current response. In this way, it can be determined whether a syntax error exists in the received text stream, and in the event that the syntax error exists, the response is processed to obtain a correct response. Therefore, the response accuracy can be improved.

A computing system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to: receive a paraphrase comprising a paraphrase text sample corresponding to an original text sample; and calculate a paraphrase metric value corresponding to the paraphrase, wherein the paraphrase metric value is calculated based on an adequacy score, a novelty score, and a fluency score of the paraphrase, the adequacy score indicating an extent to which the paraphrase text sample preserves a meaning of the original text sample, the novelty score indicating a level of difference between words and characters of the paraphrase text sample and words and characters of the original text sample, and the fluency score indicating an extent to which the paraphrase text sample is devoid of repetition, spelling, and grammatical mistakes.

An object of a system according to an embodiment is to efficiently and effectively generate examination questions and correct a paper.SOLUTION: A system includes an examination question generation part and a paper correction part. The examination question generation section learns past examination questions and generates examination questions based on parameters set by a teacher. The essay correction department analyzes essays and reports created by students, detects syntax errors, missing information, jumps in logic, citation errors, and the like, and makes concrete improvement proposals.SELECTED DRAWING: Figure 1

The application provides a data enhancement method for Chinese text proofreading, and relates to the technical field of artificial intelligence. The method judges the position and type of errors prone to occur in the correct source sentence through a sequence labeling model, makes up for the defects of current methods of randomly selecting error positions and error types, and makes the data closer to existing training data; in generating multi-word errors, syntax error data generated by using the model BERT is added, so that the semantic correlation of the generated error sentences is stronger; in the process of generating spelling errors, syntax error data generated by using the model BERT is added to simulate the situation of vocabulary selection errors in writing; at the same time, the spelling errors caused by pressing the wrong key when entering the text by using the keyboard in the real entry process are considered; the generated pseudo data contains common syntax error types, which can improve the robustness of the syntax correction model and the spelling correction model to a certain extent, and make the model learn more diverse and similar error sentence features to the real data.

The system according to the embodiment aims to provide efficient and continuous conversation practice in a language other than one's native language. [Solution] A system according to an embodiment includes a selection unit, a start unit, a response unit, a correction unit, and a scenario providing unit. The selection unit selects the language in which the user wishes to converse. The start unit starts the conversation in the language selected by the selection unit. The response unit understands what the user says to the generated AI and returns a response. The correction unit points out and corrects the user's pronunciation and grammar errors based on the response provided by the response unit. The scenario providing unit practices conversation on a specific topic based on the correction provided by the correction unit.