157 results about "Language analysis" patented technology

A method uses natural language processing for visual analysis of a dataset. The method includes receiving a first natural language (NL) input directed to a data source, from a first client. The method also includes parsing the first NL input into tokens based on a grammar and the data source. The method also includes generating and outputting an intermediate NL response, to a second client, based on the tokens and output modality of the second client. In response to receiving a user input to provide missing information: the method also includes generating an input query based on the user input; and querying the data source using the input query, to obtain a result set. The method also includes generating and outputting, to the second client, a first NL output and a snapshot of a data visualization, based on the result set and the output modality of the second client.

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention discloses a data security classification and grading method and system based on a large language model, and relates to the technical field of language analysis, and the method comprises the steps: converting bank internal business data of each layer into text data through text conversion, unifying the data form of multi-layer or multi-modal data into text data, and storing the text data in a database; and a reliable basis is provided for subsequent language semantic recognition. According to the method, the text data is partitioned and sorted, subsequent context language understanding is facilitated, the context window length under each service scene is set, the context semantic understanding requirement of each service scene is met, and the reliability and accuracy of semantic association are improved. The internal business data of the bank is classified and graded based on the risk value of the business scene and the sensitive value of the composite field, the accuracy and adaptability of data security classification and grading are improved, security protection strategies of different degrees are carried out for different levels of different types of data, and the requirements of bank data storage and prevention are met.

The invention discloses a multi-language data analysis-oriented extensible system, method, equipment and medium, and relates to the technical field of multi-language analysis, the multi-language data analysis-oriented extensible system comprises a grammar analysis module used for analyzing a query language and generating a language-specific syntax tree structure after analysis; the grammar adaptation module is used for converting a language-specific grammar tree structure into an abstract grammar tree AST in a uniform format; the abstract syntax tree processing module injects a permission control strategy, a field desensitization rule and an alarm exception expression into the AST; the business processing module is used for executing business-related rule check and label injection; the query plan generation module is used for constructing a logic query path and a corresponding distributed execution scheme; and the task scheduling module issues the distributed execution scheme to the corresponding execution node. According to the method, by constructing a unified multi-language grammar analysis framework and a standardized abstract syntax tree AST representation mechanism, compatibility and fusion of multiple query languages such as SQL, SPL and natural language are achieved.

The invention relates to the technical field of language analysis, and discloses a medical literature interpretation method and system based on a language large model, and the method comprises the steps: constructing a relationship network between words, and obtaining a first relationship network; determining a first association relationship between each text word and other words in the text, and endowing the first association relationship with a weight according to the number of association times; constructing a relationship network for words in the text according to the text content to obtain a second relationship network; determining a second association relationship between each text word and other words in the text, and endowing the second association relationship with a weight; and according to the first relationship network and the weight endowed by the first relationship network, performing optimization of the association path, and obtaining an interpretation result through content interpretation on the path. The defects of a traditional calculation method in the aspects of processing efficiency and semantic understanding are overcome. The accuracy and efficiency of literature analysis are greatly improved, and the convenience of knowledge acquisition is greatly improved.

The invention discloses a data query tool method and system based on an AI language model. The method comprises the steps that a data query request input by a target user is obtained; inputting the data query request into a basic language layer of the language analysis model to obtain basic semantic request data; inputting the basic semantic data into a general knowledge layer of a language analysis model to obtain fused semantic request data; inputting the fused semantic request data into a professional knowledge layer of a language analysis model to obtain professional semantic request data; mapping the professional semantic request data into a database to obtain an SQL (Structured Query Language) query statement; based on the SQL query statement, accessing a database to obtain a preliminary query result; and auditing and verifying the preliminary query result to obtain a target query result. According to the method, the accuracy of the query result is improved, and comprehensive consideration and design are carried out in the aspect of specialty, so that the complex and changeable data query scene requirements are met.

The invention provides a potential user mining method and system based on voice analysis, and relates to the technical field of voice analysis. Voice data published by a client on a platform is preprocessed, and the preprocessed voice data is analyzed from the aspects of emotional tendency and emotional abundance; an emotional abundant coefficient and an emotional tendency coefficient are respectively generated, a comprehensive emotional tendency coefficient of each section of effective voice is calculated through the emotional abundant coefficient and the emotional tendency coefficient, and the comprehensive emotional tendency coefficient is further analyzed to generate an emotional recommendation coefficient and a comprehensive recommendation coefficient; and evaluating potential mining values of the users according to the emotion recommendation coefficient and the comprehensive recommendation coefficient, and classifying the users. According to the method, the emotional abundance degree of the language is analyzed, the defect of tone analysis in traditional language analysis is overcome, evaluation of the mining value of the client is more accurate, pushing resources are saved, and high-quality clients are more easily mined.

The invention discloses a multi-view data category identification method based on comparative learning and private information. The method comprises the following steps: obtaining mixed features from a plurality of input views by using an auto-encoder; obtaining a private feature set and a public semantic feature set according to the mixed features, performing same-view instance neighbor private feature learning by taking the private feature set as an object to obtain useful private features, and performing cross-view semantic information consistency feature learning by taking the public semantic feature set to obtain aligned public semantic features; and a K-Means clustering prediction model is introduced to generate useful pseudo labels of private features, corresponding constraint is carried out according to the pseudo labels of the private features and the aligned prediction labels of the public semantic features, and a final label with the maximum prediction probability average value in all views is used as a classification label of multi-view data. According to the method, the accuracy of multi-view data classification is integrally improved, and the method can be applied to various fields including multi-view data, such as computer vision and natural language analysis.

The invention discloses a conversation-based multi-modal feature analysis method and electronic equipment, and relates to the technical field of language analysis, and the method comprises the following steps: collecting multi-modal evaluation data of a conversation process of a to-be-evaluated object, and carrying out the feature marking of the multi-modal evaluation data to obtain state feature points; performing statistics on occurrence frequency and feature intensity of similar features in the state feature points to obtain feature correlation parameters; extracting a historical language state feature distribution rule in different evaluation scenes from the historical evaluation data set, and calculating a language state influence factor between a language state tendency value and a feature correlation parameter in the historical evaluation data according to the historical language state feature distribution rule; the target influence factor is screened out from the language state influence factors according to the real-time language state feature distribution feature of the current conversation scene, and the effect is that clear guidance is provided for formulation of a subsequent intervention strategy.

The invention discloses a self-adaptive multi-language inter-translation system, and relates to the technical field of multi-language inter-translation, and the self-adaptive multi-language inter-translation system comprises a voice acquisition module, a language analysis module, a semantic recognition module, a bidirectional translation module and a self-adaptive learning module. The translation process is continuously optimized by analyzing the user feedback and the translation result, so that the translation quality can be continuously improved along with time and data accumulation, meanwhile, the forward and reverse translation function provided by the bidirectional translation module ensures that the user can be well supported whether inputting or receiving information, and the user experience is improved. A user can switch languages at any time according to needs, so that communication is smoother, and free flow of information and communication of multi-culture are promoted.

The invention relates to the technical field of text generation, in particular to a text generation method and system based on natural language processing. Comprising the following steps: S1, collecting corpus data, preprocessing the collected corpus data, and then performing meaning label analysis on the corpus data; s2, acquiring real-time cue words and historical cue words output by a user, and performing demand language analysis on the real-time cue words and the historical cue words by using a natural language processing technology; the method comprises the following steps: setting a professional degree benchmark, determining a professional degree evaluation dimension according to domain knowledge, carrying out professional degree scoring on candidate texts, analyzing real-time and historical demand languages of a user, determining the professional level of the user, and predicting the professional degree required by the current demand by referring to related historical output texts. The dynamic adjustment mechanism can ensure that the generated text is not too hard and obscure and not too light and brief, and the applicability and practicability of the text are greatly improved.

The invention relates to the technical field of text language analysis, in particular to a method for evaluating emotion in client evaluation text content, which comprises the following steps of: S1, establishing a basic emotion database through the client evaluation text content, performing similarity screening, invalid deletion and coding on data, and removing texts without evaluation value to obtain a basic emotion database; carrying out natural language preprocessing on the residual evaluation texts; s2, performing sentiment classification on the evaluation of the customer based on a deep learning model, tracking the change of the evaluation of the customer through time sequence analysis, judging the change of the customer on products and services at different time points by using clustering analysis, and constructing a personalized portrait of the customer, in the method, the change of the evaluation of the customer is analyzed and tracked through A I, and a theme label is distributed for an evaluation text; and meanwhile, when the BERT algorithm model is trained, the training effect of the model is enhanced through MLM mask, NSP prediction and customer personalized portrait, and the purpose of truly extracting semantics and emotions in the text is achieved.

The invention belongs to the technical field of single-cell multi-omics, and discloses a single-cell multi-omics data analysis system, method and device and a storage medium, a data import module is used for reading sequencing data from different sequencing platforms and storing the sequencing data as S4 objects; the cross-language analysis module is used for calling a single-cell multi-omics analysis tool based on an R language and a Python language to analyze an S4 object according to an analysis process in a unified framework; the deep learning optimization module is used for training a deep learning model based on the cross-language interaction interface and integrating an analysis result of the deep learning model to an object S4; and the interaction and extension module is used for obtaining an analysis process and visualizing an analysis result, constructing a user-defined single-cell multi-omics analysis tool in a plug-in form and loading the tool to the S4 object for analysis. Cross-sequencing platform data compatibility, multi-language tool seamless integration and deep learning framework efficient access are achieved, and the problems of data isomerism, language ecological splitting and algorithm expansibility in the prior art are solved.

A data processing method, which can be applied to the field of artificial intelligence. The method comprises: acquiring a first text and a second text, wherein the first text is a text needing to be discriminated, and the second text is the discrimination content of the first text; determining a first prompt and a second prompt on the basis of entities comprised in the second text, wherein the first prompt instructs to recognize the descriptions of entities in the first text, and the second prompt instructs to discriminate, on the basis of the descriptions of the entities, the first text indicated in the second text; and on the basis of the first text, the first prompt, and the second prompt, obtaining a discrimination result of the first text by means of a language model. According to the present application, the language model is guided by means of the first prompt to recognize the interpretations of the entities in the first text, the language model is used to automatically split an understanding task of an element into prompts of NLP tasks for inputting, and the prompts are used to guide the language model to perform text discrimination on the basis of the descriptions of the entities, so that the language analysis efficiency and accuracy are improved.

The invention discloses a split adjustment demonstration system based on artificial intelligence, particularly relates to the technical field of video split, and comprises a split input semantic analysis module which is used for analyzing a text and a preliminary visual material input by a user and converting the text and the preliminary visual material into structured data for subsequent processing. According to the method, text description is converted into structured data and scene elements are extracted through the sub-lens input semantic analysis module, so that accurate data support is provided for follow-up lens language analysis, composition optimization and scene scheduling, and the lens analysis and optimization module identifies potential problems and provides optimization suggestions by analyzing lens languages and composition, so that the lens analysis and optimization efficiency is improved. The intelligent adjustment video generation module automatically adjusts lens splitting parameters according to optimization suggestions and generates a preliminary demonstration video, video details can be further adjusted through the interactive editing module, it is ensured that the video quality reaches the expectation, and the intelligent level of the system is improved; therefore, the flexibility, the innovativeness and the efficiency of lens splitting adjustment are improved.

A system for managing access to services in a computing system. The system includes processes to allow users to request access to services, and the automated review of such requests. The system is configured to consider a wide range of factors, including the content of the request and the service requested, information relating to the user, and metadata relating to the service, as well as user behaviours. Natural language analysis is utilised to analyse a user's request for access and the service to which access is requested.

The invention discloses a method and a system for searching by combining user input information, and particularly relates to the technical field of language paragraption.The method comprises the following steps: firstly, converting a plurality of user inputs into an input semantic structure containing keywords, semantic relationships and context tags; comparing the structures, extracting interactive focuses and generating a semantic consensus score; constructing a fusion path diagram according to the consensus score, the keyword weight and the semantic adaptation degree; semantic fusion and conflict mediation are executed according to the path diagram, and a unified expression structure is generated; and finally, sorting the keywords according to semantic levels and task orientation, and constructing a comprehensive search expression to finish query output. According to the method, through semantic analysis and cross comparison input by multiple users, the consensus content is extracted, the fusion path diagram is constructed, and it is ensured that the fusion sequence is reasonable and semantic consistency is achieved; and finally, a comprehensive semantic expression structure is generated and a search expression is constructed, so that accurate alignment of query contents and user intentions is realized, and representativeness and accuracy of search results are improved.

The invention discloses a system and a method for automatically assembling a data analysis process by a large language model based on Faiss, and belongs to the technical field of data analysis and artificial intelligence. The system comprises a client, a server and a data analysis platform. The method comprises: receiving a natural language analysis demand of a user; identifying a user intention as process assembly or suggestion through a large language model; encoding the demand into a query vector, and performing similarity retrieval by using a Faiiss vector library pre-constructed based on the analysis tool description information to obtain a first candidate tool set; inputting the set and original requirements into a large language model for fine arrangement and logic assembly to generate a second candidate tool set; and finally converting into a structured process file which can be directly identified and executed by a data analysis platform. According to the method, the problem that a structured data analysis process cannot be automatically constructed in the prior art is solved, end-to-end intelligent generation from natural language description to an executable analysis scheme is realized, the use threshold is remarkably reduced, and the analysis efficiency is improved.

The invention discloses a code quality analysis method and device, equipment and a medium, which are used for quickly and efficiently carrying out code quality analysis. An open source software project server can identify the code type of a code to be subjected to quality analysis, and calls and executes a command which is stored in the open source software project server and corresponds to the code type; and analyzing the quality of the code based on the command to obtain a real-time analysis result, and continuously analyzing the quality of the code based on the command until the quality analysis of the code is completed if the sub-result can be ignored when the sub-result containing the error of the code in the real-time operation result is identified and the sub-result can be ignored. Obtaining a machine language analysis result of the code; in addition, the open source software project server can analyze the machine language analysis result based on a code analysis plug-in which is pre-configured in the code quality inspection server and corresponds to the code type, so that the code quality analysis efficiency can be greatly improved.

The invention provides a complex scene visual recognition rapid construction method based on natural language description, and relates to the technical field of computer vision and natural language processing, and the method comprises the steps: obtaining natural language description information inputted by a user; performing language analysis and element extraction on the natural language description information to obtain a scene semantic table; decoupling the scene semantic table into M atomic conditions, and obtaining an atomic instruction set; performing visual feature extraction on the to-be-recognized image by using a visual encoder to generate an image feature vector; converting the atomic instruction set into an atomic instruction vector set, and outputting an atomic instruction verification set; generating a relation verification matrix; and executing global logic expression verification operation based on the atomic instruction verification set and the relationship verification matrix, and outputting comprehensive confidence. The technical problems that in the prior art, semantic understanding depth is insufficient, complex scene understanding is limited, and self-adaptive capacity is insufficient are solved.

The invention discloses a virtual human action driving method and device based on language analysis and a storage medium, and belongs to the technical field of virtual human. The method comprises the following steps: acquiring motion posture data of a virtual human and voice data at the current moment, wherein the motion posture data comprises motion information of each joint of the virtual human; according to the voice data and the action posture data, determining evaluation indexes of action fluency degrees of all first action data corresponding to the voice data; virtual human actions are generated according to the first action data and second action data, the second action data are transition action data determined according to the first action data and the evaluation indexes, and the technical effect of improving the fluency degree of the virtual human actions is achieved.

The invention discloses a language analysis method and system based on an index semantic layer and a large language model. The invention relates to the technical field of data visualization analysis. Reading metadata constructed by a predefined data index model; metadata is used to construct a Prompt full word of an index, and a basis is provided for subsequent natural language query; generating vector representation for the same metadata in the index semantic layer; the vectors are used for being subsequently matched with natural language requests input by a user; in the technical scheme of the invention, a user can use a natural language to carry out data query and visual analysis, and does not need to learn a complex query language or an operation interface. According to the method, the hidden intention and context in the user query can be understood, the user query is matched with the metadata vector in the index semantic layer, the query intention of the user can be recognized more accurately, and analysis errors are avoided. And the reasoning ability of the large language model further enhances the query accuracy.

The invention discloses a knowledge graph link prediction method, device and equipment, a medium and a program product, and the method comprises the steps: generating a candidate tail entity set based on a head entity element and a relation element of a to-be-queried triple in a knowledge graph, the candidate tail entity set comprising a plurality of candidate tail entity elements, performing semantic analysis on the to-be-queried triad through the language analysis model to obtain semantic information of the to-be-queried triad, screening the candidate tail entity set based on the semantic information, generating a to-be-scored tail entity set according to a screening result, and performing comprehensive scoring on each to-be-scored tail entity element in the to-be-scored tail entity set to obtain a scoring result. According to a comprehensive scoring result, target tail entity elements are screened out from a to-be-scored tail entity set, link prediction is carried out on the knowledge graph based on the target tail entity elements and the to-be-queried triad, potential features in the triad are effectively mined, accurate completion of missing triad entities is achieved, and the accuracy of the whole knowledge graph is improved. And the accuracy of knowledge graph link prediction is greatly improved.

To provide a schedule adjustment system for improving convenience of users.SOLUTION: A communication data acquisition unit (201) of a schedule adjustment system (1) acquires communication data representing content of communication performed with a communication tool. An adjustment schedule data acquisition unit (203) acquires adjustment schedule data representing an adjustment schedule to be adjusted through communication on the basis of communication data and a machine learning model capable of executing language analysis. A schedule transmission unit (206) transmits the adjustment schedule data to a schedule management tool cooperating with the communication tool.SELECTED DRAWING: Figure 5

Improve user convenience. [Solution] A communication data acquisition unit (201) of a schedule adjustment system (1) acquires communication data indicating the content of communication made using a communication tool. An adjustment schedule data acquisition unit (203) acquires adjustment schedule data indicating adjustment schedules to be adjusted through communication based on the communication data and a machine learning model capable of language analysis. A schedule transmission unit (206) transmits the adjustment schedule data to a schedule management tool linked to the communication tool.

The invention discloses an expression language analysis method and device and a storage medium, and relates to the technical field of electronic digital data processing.The expression language analysis method comprises the steps that in response to an expression text input by a user in an editor, an analyzer integrated to a current program is called; analyzing an abstract syntax tree corresponding to the expression text based on the analyzer; and in response to function operations for the abstract syntax tree, outputting a function execution result at the editor, the function operations including syntax highlighting, code checking, automatic completion and expression calculation. According to the method and the device, the technical problem that expression languages and analysis tools in related technologies are closed sources and cannot be compatible with own systems is solved, and the technical effect of automatically generating the analyzer to analyze the expressions of the currently defined grammar rules is achieved.

The invention provides an inline assembly code analysis method and system based on large language model assistance. The inline assembly code analysis method comprises the steps that the position of an inline assembly code potentially influencing the static program analysis effect is positioned and measured in a C source code item; target code abstract point selection: constructing a calling graph based on the identified inline assembly code and selecting a position suitable for code abstract in the graph; automatic code abstract generation assisted by the large language model: guiding the large language model to analyze an inline assembly function through a cue word project and generating a code abstract in a specific format; verifying the correctness of the code abstract, namely checking the correctness of the generated code abstract through methods such as large language model self-verification; and deploying and applying the code abstract, namely deploying and applying the verified code abstract in the program analysis framework to be optimized. The method is used for overcoming the defect that an existing source code level static program analysis framework has difficulty in cross-language analysis between a source code and an inline assembly code.

The embodiment of the invention provides a driving behavior adjustment method and device based on driving simulation equipment, a storage medium and a program product. In the embodiment of the invention, an emotion analysis model and a language analysis model based on artificial intelligence are provided, and at least one image corresponding to a driver is collected in a process that the driver uses a driving simulation device to practice a target driving project. Calling an emotion analysis model to analyze user emotion information of the driver according to the image including the face; if the emotion information of the user is negative emotion, the reason and contact suggestion information for generating the negative emotion based on the language analysis model is called according to the exercise data of the driver on the target driving project and in combination with the signal data of the vehicle part of the driving simulation equipment, and the user can conveniently perform the negative emotion according to the personal conditions of different drivers. Individual differences are considered, intelligent feedback and personalized solutions are provided, and more efficient and high-quality driving training services are provided for users.

The invention relates to a method for functional analysis of a source system (1), wherein a syntax tree model (3) is generated from the program code data of the source system (1). In order to propose a method for functional analysis of a source system (1), in particular with extensive program code data, which method makes it possible to better identify interrelated functional units and, with high result quality and at the same time low resource requirements, to improve further processing by means of a large language model, the invention provides that a syntax analysis unit (4) generates a syntax embedding model by decomposing the syntax tree model (3) into individual syntax element embeddings (5), each representing a syntax element, and stores said syntax embedding model in a syntax embedding model memory (6), and that a language analysis unit (8) generates a language embedding model by decomposing the program code data into individual language element embeddings (9), each representing a language element, and stores said language embedding model in a language embedding model memory (10), whereupon, by means of a query unit (12), at least one query embedding (13) is generated from a query (11) representing a functional element of the source system (1) and those syntax element embeddings (5) and language element embeddings (9) which lie within a predefined range of similarity to the query embedding (13) are retrieved from the syntax embedding model memory (6) and the language embedding model memory (10), whereupon the syntax elements represented by the retrieved syntax element embeddings (5) and the language elements represented by the retrieved language element embeddings (9) are transferred as result elements (14) to an output unit (15), which generates therefrom a functional description dataset (16) and outputs the latter.