20 results about "Analytic language" patented technology

The invention provides a text reinforcement learning method and device, electronic equipment and a computer storage medium, relates to the technical field of text reinforcement learning, and is applied to a text generation model. The method comprises the following steps: determining one or more target vocabularies in a statement to be analyzed, and generating a replacement word set according to the target vocabularies; replacing the target vocabulary in the to-be-analyzed statement with a current candidate replacement word to obtain a replacement statement; calculating one or more difference index values between the replacement statement and the to-be-analyzed statement; wherein the difference index value is used for quantifying the influence of the replacement of the target vocabulary on the original sentence from different dimensions; based on the difference index value, determining an importance weight of the target vocabulary in the to-be-analyzed statement; wherein the importance weight is used for redistributing the reward of the single character in the sentence. Vocabulary importance is quantified through disturbance analysis and multi-dimensional evaluation, and then fine-grained distribution of rewards is achieved.

The method for automatic identification, classification and development trend analysis of net red villages based on multi-source data fusion and natural language processing comprises the following steps: UGC data is crawled from Xiaohongshu and Douyin through a distributed master-slave architecture, de-duplicated based on SimHash, and normalized in time and coding format; a text semantic fingerprint is generated, and multi-level semantic cache fingerprint matching is performed; for unassigned text, its complexity is calculated, and a large language model API is adaptively called to automatically complete and extract five-level administrative divisions; weights are determined based on the analytic hierarchy process, interaction indicators such as likes, comments, collections and forwards are integrated, and a comprehensive network heat index of the village is obtained; an external text mining tool is connected, and batch word frequency analysis, semantic network analysis and sentiment tendency evaluation are performed; a document-term matrix is constructed, TF-IDF weighting is performed, and unsupervised clustering algorithm is used for clustering analysis of village characteristics; cross-dimension analysis is performed on the clustering results, and a development portrait, advantage mining and operation suggestion warning are automatically generated in combination with the SWOT model.

The invention relates to an evaluation information output method and device of a large language model, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining an evaluation data set corresponding to each evaluation link of a preset dialogue state evaluation process; the evaluation data set corresponding to each evaluation link comprises to-be-analyzed statements under different evaluation dimensions and a corresponding dialogue state analysis result set; the dialogue state analysis result set comprises a correct analysis result and at least one analysis result different from the correct analysis result; inputting an evaluation data set corresponding to the evaluation link into a pre-trained large language model, and outputting a target prediction result corresponding to the to-be-analyzed statement; and comparing a correct analysis result corresponding to the to-be-analyzed statement in the evaluation data set with a corresponding target prediction result to obtain evaluation information of the pre-trained large language model in each evaluation link. By adopting the method, the dialogue state perception capability of the large language model can be evaluated more accurately.

This invention discloses a dual-prevention intelligent interaction system based on a large language model, belonging to the field of artificial intelligence technology. The system includes a large language foundation model, a knowledge base management module, an agent module, a multimodal large model, and an evaluation module. The large language foundation model understands user intent and processes natural language tasks, and provides token processing capabilities for other modules. The knowledge base management module is used to classify, store, and manage professional knowledge, establish indexes based on semantics, and finally store it locally. The agent module analyzes user input, with different workflows encapsulated within different agents to provide personalized services to users. The multimodal large model module receives user-input speech, converts the speech to text, transmits the information description to the agent, generates answers through semantic analysis, and converts the text back to speech to send to the user. The evaluation module establishes an evaluation index system from four perspectives: accuracy, answer relevance, context relevance, and context recall.

This invention relates to the field of information retrieval structure technology, specifically to a method and system for monitoring data during the investment promotion process in industrial parks. The method includes the following steps: extracting investment promotion text, identifying combinations of verbs and resource nouns, analyzing word order and semantic features, filtering action-driven segments, dividing structural sequences and identifying connection relationships, establishing semantic jump paths, reorganizing sentence order, and generating a data monitoring structure template for investment promotion in industrial parks. In this invention, by extracting verbs and resource nouns to construct sentence content, combining word order features and semantic tags to classify and filter segments, dividing structural sequences based on the position of resource nouns and merging content with consistent semantic direction, mapping connectors to jump paths to construct task behavior sequences, and realizing the logical organization and sequential reorganization of sentences, this generates a data expression method with traceability and structured features, improving the semantic recognition efficiency and data monitoring capabilities during the investment promotion process in industrial parks.

The application provides a text reinforcement learning method and device, electronic equipment and computer storage medium, relates to the technical field of text reinforcement learning, and is applied to a text generation model. The method comprises the following steps: determining one or more target words in a to-be-analyzed sentence, generating a set of replacement words according to the target words, replacing the target words in the to-be-analyzed sentence with a current candidate replacement word to obtain a replacement sentence, calculating one or more difference indicator values between the replacement sentence and the to-be-analyzed sentence, wherein the difference indicator values are used to quantify the influence of replacement of the target words on the original sentence from different dimensions, determining the importance weight of the target words in the to-be-analyzed sentence based on the difference indicator values, and wherein the importance weight is used to redistribute the rewards of individual characters in a sentence. The importance of the words is quantified through perturbation analysis and multi-dimensional evaluation, and then fine-grained allocation of rewards is realized.

The invention relates to a construction method and device of an evaluation data set of a large language model, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining dialogue state theme statements corresponding to different evaluation dimensions; expanding the dialogue state theme statement to obtain an expanded dialogue state theme statement; optimizing the expanded dialogue state theme statement to obtain a to-be-analyzed statement; the richness of the dialogue state information of the to-be-analyzed statement is higher than the richness of the dialogue state information of the expanded dialogue state theme statement; taking the to-be-analyzed statement and the corresponding dialogue state analysis result set as an evaluation data set; the dialogue state analysis result set comprises a correct analysis result and at least one analysis result different from the correct analysis result; the evaluation data set is used for evaluating the dialogue state perception ability of the pre-trained large language model. By adopting the method, the dialogue state perception capability of the large language model can be evaluated more accurately.

The application provides an aspect-level sentiment analysis method and device based on contrast learning. The method comprises the following steps: S1, generating a plurality of prompt sentence pairs based on a plurality of preset aspect sentiment pairs; S2, inputting a to-be-analyzed sentence into an aspect-level sentiment analysis model based on contrast learning to obtain an analysis result, wherein the aspect-level sentiment analysis model comprises: an enhancement module, which combines the to-be-analyzed sentence with a question or answer prompt sentence in different prompt sentence pairs to obtain different to-be-analyzed enhanced sentences; a pre-training coding layer, which obtains a sentence representation vector and a word vector of the to-be-analyzed enhanced sentence; a first activation function layer, which obtains a first analysis result; and a second activation function layer, which marks the position of a target word matched with the aspect sentiment pair to obtain a marked sequence. The joint detection of the target, the aspect and the sentiment is realized, high-quality semantic information can be generated after the to-be-analyzed sentence is enhanced by using the question or answer prompt sentence, the problem of sparse marked data is effectively alleviated, and the sentiment analysis effect is improved.

The invention discloses a statement analysis method and device, electronic equipment, a storage medium and a program product. Obtaining a to-be-analyzed vector corresponding to the to-be-analyzed statement; determining a query tree vector corresponding to the query tree; splicing the vector to be analyzed and the query tree vector to obtain a spliced vector; and inputting the splicing vector into an exception analysis model, and outputting a root cause which causes the execution time exception of the to-be-analyzed statement. According to the method, the features of the to-be-analyzed statement are captured more accurately and more comprehensively, the dimensionality of the features of the to-be-analyzed statement is enriched, statement analysis focuses on the to-be-analyzed statement, end-to-end statement analysis is achieved, the root cause causing the execution time abnormity of the to-be-analyzed statement is automatically output, and statement analysis is more efficient.

The invention provides an analysis-free log anomaly detection method and device based on space-time semantic features, and the method comprises the steps: converting labeled log data into structured vector representation based on a preset bag-of-words model, and carrying out the clustering and grouping of the vector representation through a clustering algorithm, and calculating the feature value of each word in each group by adopting word frequency-inverse document frequency to form semantic feature representation of each log data, so that the extracted features can pay attention to local features. To-be-analyzed log data is matched to a target cluster based on the clustering algorithm, and semantic feature representation of the to-be-analyzed log data is calculated; and finally, calculating a normalized compression distance between the to-be-analyzed semantic feature representation and the labeled semantic feature representation, carrying out classification based on a KNN model, and determining a label of the to-be-analyzed log data. The method does not depend on a complex model structure and a training process, does not need additional training parameters, and has better generalization and applicability.

The invention discloses a method and system for improving NL2SQL accuracy through natural language problem rewriting, and relates to the technical field of natural language processing. Aiming at SQL generation errors caused by language uncertainty and database structure differences, the method comprises the following steps: performing lexical analysis, syntactic analysis, semantic disambiguation and information completion on natural language query statements; through synonym replacement and sentence pattern transformation, generating rewritten query statements with consistent semantics from the completed query statements; evaluating the semantic similarity, the structure matching degree and the logic rationality of the original statement and the rewritten statement by utilizing a pre-training language model and database metadata, and screening an optimal rewritten statement; based on a mapping rule base and a query template, automatically generating an SQL statement from the optimal rewriting statement, and optimizing the SQL statement; and verifying a query result returned after the SQL statement is executed, modifying the SQL statement manually when verification fails, and synchronously updating the mapping rule base. According to the method, high-precision conversion from the natural language query statement to the SQL statement is realized.

The invention discloses an intelligent contract review method and system based on a large model. The method comprises the following steps of obtaining a contract text needing to be reviewed; obtaining an input review demand; processing the contract text by comprehensively applying lexical analysis, grammatical analysis, semantic analysis and chapter analysis according to the review requirements so as to extract key information and contract terms; according to the extracted key information and contract terms, performing optimization and deficiency analysis on the contract text based on a large model, identifying favorable factors and adverse factors and processing suggestions; and summarizing the recognized favorable factors, negative factors and processing suggestions to generate an analysis report. According to the invention, by using the large-scale pre-training model and integrating the multi-field data, the comprehensiveness and accuracy of contract review are significantly improved, and the ability to understand novel contract terms and evaluate risks is enhanced.

The invention relates to a method for analyzing a language structure by using a language expression, which is suitable for analyzing structures of more than 7,000 languages on the earth. The method comprises the following steps of: analyzing a morphologic element and a syntactic structure of a target sentence by a morphologic element and syntactic analysis unit; and based on a pre-training language expression learning algorithm provided by a language expression learning algorithm unit, a language expression calculation unit generates a language expression for the morphologies and syntactic structures of the sentences. Wherein the language expression calculation unit is used for dividing a sentence into an upper left area, a lower left area, an upper right area and a lower right area by taking a sentence center symbol as a benchmark, a subject symbol is distributed in the upper left area, a core verb symbol is distributed in the lower left area, and a verb type symbol is distributed in the lower right area; non-predicate symbols representing remaining phrases other than subjects and verbs are assigned to the right upper region. Furthermore, symbolization specific information including marks (1-4) indicating a person and a character number mark (.) indicating the number of word characters such as nouns and adjectives is added to at least one of the left upper mark, the left lower mark, the right upper mark and the right lower mark of each symbol. By means of the method, the sentences in the specific language can be accurately analyzed into structured syntactic expressions, and therefore the efficiency of language processing such as sentence recognition, semantic understanding, translation and retrieval is improved.

The present invention provides a fine-grained information extraction method and system based on part-of-speech tagging, the method comprising: encoding and information extraction strategies of pre-stored typical example sentences; performing phrase-level word segmentation on the sentence to be analyzed and tagging the phrases with parts of speech; merging and hiding adjacent phrases according to the encoding strategy; replacing nouns, verbs, and verb phrases in the sentence with parts of speech based on the parts of speech and sentence structure marked by S to form an encoding of the sentence to be analyzed; matching the encoding of the sentence to be analyzed with the encoding of pre-stored typical example sentences; if there is a match with the pre-stored typical example sentences, extracting information from the sentence to be analyzed according to the extraction strategy of the matched typical example sentences; if there is no match, extracting information from the sentence to be analyzed according to the information extraction strategy; and storing the encoding of the sentence to be analyzed and the information extraction strategy. The information extraction process of the present invention can quickly adapt to new information extraction needs.

The invention discloses a multilingual real-time semantic alignment translation system based on a cross-language pre-training model, and relates to the technical field of language translations, and the multilingual real-time semantic alignment translation system comprises the following steps: constructing an original translation statement and a filling analysis statement of the same semantic phrase based on the cross-language pre-training model; using an alignment analysis method to obtain vocabulary structure features; translating the to-be-translated statement by using a cross-language pre-training model based on the vocabulary structure features of the same semantic phrases corresponding to the to-be-translated statement; the method is used for solving the problems that in an existing multilingual real-time semantic alignment translation method, accurate and contextual semantic alignment translation cannot be carried out on specific vocabularies based on the context relation in sentences before and after translation, so that partial vocabularies in the translated sentences do not accord with the meaning of the original text, and the semantics of the original text cannot be accurately expressed.

The present application relates to the technical field of medical data mining, in particular to a system for grading aphasia in a multilingual scenario. The system first acquires language data in a conversational scenario and non-language data in an instructional scenario from a patient through a data acquisition module; further acquires a language organization coefficient according to fluent language expression of the patient in the conversational scenario in a performance analysis module; acquires a semantic understanding coefficient according to principal component features of language characteristic data of the patient in the instructional scenario in combination with principal component features of non-language clue data; and finally grades aphasia of the patient through a grading evaluation module. The system supplements and corrects language expression understanding ability of the patient from a non-language perspective by combining principal components of non-language data of the patient on the basis of language expression in traditional analysis of language data, thereby improving accuracy and comprehensiveness of grading evaluation.

The invention provides a corpus quality evaluation method and device. The method comprises the following steps: acquiring a corpus text; quality evaluation results of the corpus text are determined, and the quality evaluation results comprise a first quality evaluation result of sentences in the corpus text, a second quality evaluation result of paragraphs and a third quality evaluation result of the corpus text; wherein the first quality evaluation result is obtained by analyzing sentences based on a pre-trained syntactic structure model, the paragraph is obtained by analyzing a semantic relationship among a plurality of sentences based on a pre-trained semantic fragmentation model, and the second quality evaluation result is obtained by analyzing the paragraph through a pre-trained logic association model; the third quality evaluation result is obtained by analyzing the corpus text through a pre-trained information density model. In this way, the model training speed and the model performance are improved through the corpus training model with higher quality.

The application provides a construction method of a general query language of a multi-storage system, which comprises the following steps: designing a syntax of the general query language; writing a description script of the syntax rule; using a compiler to generate executable syntax analysis code by taking the syntax rule file as input; inputting a specific sentence of the general query language, processing by lexical analysis and syntax analysis, and generating a syntax analysis tree; analyzing the content in the syntax analysis tree, combining with the type of a target storage system, and translating the syntax analysis tree into a target query language; executing the target query language to realize the query of the target storage system; and converting the query result into a unified structure to provide a query client. The application simplifies the selection of the storage system and improves the freedom degree of the selection of the storage system; a business system developer can realize the data query of the multi-storage system by learning only one syntax; the application has high expansibility, is convenient for porting and compatible with the existing storage system, and improves the development efficiency of the application software.

This invention relates to the field of data processing technology, specifically to a method and system for processing data based on a large language model. The method includes the following steps: based on inter-sentence punctuation location and part-of-speech tagging, dividing sentence blocks into functional partitions; extracting themes and relational words to determine the starting points of semantic chains; constructing trigger index sequences; identifying logical jump points and breakpoint locations; mapping the tag structure to the language model output to analyze deviations; and generating a list of tag mapping combinations. In this invention, by analyzing the trend of semantic direction changes, semantic turning points can be captured, and their relationship with verb-noun combinations can be determined. The trigger points for actual information transfer can be extracted. Through the location of keyword starting and ending blocks and the logical judgment of noun group cross-combinations, semantic path breakpoint regions can be identified, giving the path integrity clear breakpoints. A traceable semantic mapping structure path is established within the language model, enhancing the accuracy, coherence, and hierarchical clarity of semantic reconstruction.

The invention discloses a data processing method and device, equipment and a storage medium, and is applied to the field of artificial intelligence. According to the scheme, after the target word is collected, the first statement, the second statement and the third statement containing the target word are generated according to the target word, then the at least three reference words are generated, the first statement, the at least three reference words and the target word are processed, and the first question, the second question and the third question are obtained. The first problem is a problem selected according to the understanding of the statement after the statement is analyzed, the second problem is a problem of word selection and blank filling, and the third problem is a problem of semantic judgment. In the data processing method, after the target word is determined, a series of problems can be automatically generated according to the target word, human participation is not needed, and the construction efficiency of the test benchmark is greatly improved.