913 results about "Text categorization" patented technology

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention relates to the technical field of text classification, and provides a text classification method based on a large model and a rule engine, and the method comprises the steps: S1, storing multi-level rule classification labels, and constructing a classification rule template library; s2, receiving text data from various data sources, and preprocessing the text data; s3, performing rule matching on the text data based on the classification rule through a rule engine, and outputting a rule classification result; and S4, when any one of the following conditions is met, large language model classification is triggered: a, a classification rule is not matched; b, matching a classification rule, wherein the rule confidence is smaller than a rule confidence threshold; c, the text data length exceeds the preset text data length; d, matching a specific business scene label; outputting a model classification result; and S5, when the rule engine classification in the S3 and the large language model classification in the S4 are parallel, executing the strategy. The output reliability and the service continuity are guaranteed, and the method is suitable for scenes with high accuracy requirements such as financial compliance examination and the like.

The invention discloses a science and technology public text intelligent classification and service method and device based on deep learning. The method comprises the steps that multi-source science and technology public text data are acquired and preprocessed; extracting keywords by adopting a keyword extraction algorithm, and splicing the keywords with the public text title to form enhanced text features; constructing a multi-dimensional public text classification system and performing data annotation; performing feature extraction and fine adjustment by adopting a BERT pre-training model to obtain a classification model; automatically classifying the newly-added public texts and visually presenting the newly-added public texts; and generating a personalized recommendation result based on the user portrait and the public text feature index. The invention further relates to a technical scheme of multi-objective quality diversity optimization, heterogeneous resource allocation and fusion of the LPLC2 neural network and the BERT. The technical problems that a traditional method is limited in complex semantic understanding ability, single in classification dimension and lack of an integrated solution are solved, and the accuracy of science and technology public text classification and the intelligent level of service are improved.

The invention relates to a Han-Cross industry text classification method and system based on prompt learning and adaptive loss weighting, and belongs to the technical field of natural language processing. The method comprises the following steps: designing and constructing a universal prompt template; the method comprises the following steps: recombining a Han-Cross cross-border industry text classification data set, namely converting an original single sample into paired samples; in a few-sample and multi-language scene, related vocabularies are adopted as external knowledge resources, and vocabularies most related to the mapping labels are retrieved from the related vocabularies; expanding the vocabulary mapper by introducing synonyms and associated vocabularies; adopting a dynamic mixed loss function and applying the dynamic mixed loss function to a pre-training language model to optimize a few-sample classification task; and classifying Chinese and Vietnamese cross-border industry texts by using the optimized pre-training language model. The method shows a remarkable effect in Chinese and Vietnamese industry text classification tasks, and is particularly suitable for a few-sample learning scene with data scarcity and language imbalance.

The invention relates to a zero sample text classification method and device based on cross-modal information completion. According to the method, cross-modal information complemented image construction is realized by designing a new image label mapping mechanism and based on context text generation, finally, a multi-modal collaborative reasoning framework is designed in a reasoning stage, text semantic perception capability is enhanced by prompting project optimization text input, and zero sample reasoning capability of a CLIP model is effectively improved. Aiming at the semantic deviation problem of automatic label generation, a cross-modal information complemented image is constructed based on context text generation so as to optimize the text semantic representation integrity, and meanwhile, a prompt design is introduced in a reasoning stage to enhance the text semantic perception capability; according to the two-stage keyword automatic selection mechanism, firstly, a keyword candidate set is generated through a large model, secondly, through multi-modal matching and selection, the optimal keyword phrases are selected to serve as text information complemented by cross-modal information, and semantic accuracy and field adaptability are effectively improved.

The invention discloses a BERT model-based resume screening method and system, and belongs to the technical field of deep learning and text classification. The method comprises the following steps of: obtaining post demand text data and job seeker resume text data, and preprocessing the post demand text data and the job seeker resume text data; based on a TFIDF-LDA model, subject term extraction and category feature expansion are carried out on the preprocessed text vector; performing word extraction vocabulary filtering on the subject word vector after category feature expansion based on N-gram of information entropy; a deep learning matching degree calculation module is combined to calculate the comprehensive matching degree of the posts and the job seekers, and the resumes of the job seekers are preliminarily screened in combination with a set threshold value; and carrying out resume text classification based on the FE-BERT model to obtain a resume screening result. According to the method, the problems that the ability of candidates is difficult to accurately evaluate and different post requirements cannot be adapted or the talent screening accuracy is low due to the reasons of inaccurate keyword matching, incapability of understanding contexts and the like in the existing resume screening method can be solved.

The invention discloses a text classification method based on multi-expert fusion gating graph neural network comparative learning, which comprises the following steps: preprocessing an original text, generating a standardized corpus, and extracting words, part-of-speech and entity features; constructing a first heterogeneous graph based on word features, constructing a second heterogeneous graph based on part-of-speech features, and constructing a third heterogeneous graph based on entity similarity; dynamically weighting and fusing the first heterogeneous graph, the second heterogeneous graph and the third heterogeneous graph through an expert gating fusion module to generate a fused graph structure; performing graph convolution coding on the fused graph structure to generate a node representation vector; executing double-layer comparative learning based on the node representation vector: optimizing single sample representation consistency by implementing instance-level comparative learning, and synchronously implementing cluster-level comparative learning to optimize intra-class center aggregation; and inputting the representation vector subjected to comparative learning optimization into a classifier, and outputting a text category label. According to the method, the comprehensive performance of the short text classification model in the aspects of semantic expression, structural modeling and cross-sample discrimination can be effectively improved.

The invention relates to the technical field of natural language processing and deep learning matching recommendation, and discloses a supply and demand matching method for digital science and technology personalized services, which comprises the following steps: carrying out semantic understanding and analysis on a submitted technical long text by adopting a natural language large model, effectively extracting a core text of technical contents, and carrying out semantic analysis on the core text; and based on the text classification model, accurately delimiting industry field labels, thereby solving matching obstacles caused by cross-industry term differences. Precise extraction of clear numerical parameters and performance indexes in technical texts is realized by using a natural language large model, a refined demand text set is established, and deep semantic vectorization representation is performed through a word embedding model; through cosine similarity calculation and a screening rule based on industry labels, the matching accuracy and reliability of cross-industry technical services are greatly improved; based on an association weight mechanism of performance indexes, semantic similarity and performance index association degree are comprehensively considered, and the accuracy of matching recommendation results is optimized.

The invention discloses a natural language question and answer method, system and device, a medium and a product, and relates to the technical field of semantic recognition. The method comprises the steps that a user question is recognized based on a named entity recognition model, and a key entity is determined; the key entity comprises a place name, a distance and an orientation in the question; performing text classification on the user question based on a user intention recognition model, and determining the intention of the user; determining a structured query statement based on a preset language model according to the key entity and the intention; obtaining multi-dimensional geographic space data in the Internet map; converting the multi-dimensional geographic space data into a triple, and constructing a structured knowledge graph; and according to the structured query statement and the structured knowledge graph, determining an answer to the user question. According to the method, the intention of the question of the user is accurately and intelligently understood, and the accurate answer is returned.

Methods and systems for Few-Shot Class-Incremental Learning (FSCIL) that utilizes a combination of Session Specific Prompts (SSP) and hyperbolic distance metrics to enhance session-wise learning and representation of image-text pairings across differing classes. The methods and systems include a base training session where both text and image features are projected into hyperbolic space for accurate class pairing using a cross-entropy loss function. Subsequent incremental sessions incorporate previously learned SSPs to retain and augment the separability of classes while minimizing the trainable parameters. This enhances performance in image-text classification tasks by leveraging a minimalistic approach, achieving higher accuracy with fewer trainable parameters compared to traditional models.

The invention discloses a Chinese text classification method based on metaphor association and label constraint comparative learning, and belongs to the technical field of natural language processing and artificial intelligence. A classification model construction method comprises the steps that an ambiguity recognition module is constructed to quantify voice and syntactic ambiguity of characters, and high ambiguity expression is recognized; a cue word management module is constructed, metaphor interpretation and label definition description are generated through a large language model, and the text semantic understanding depth is enhanced; designing a context attention mechanism and a metaphor attention mechanism, dynamically adjusting a feature weight and realizing label definition alignment; and constructing a loss function based on triple contrast learning, enhancing relevance between metaphor content and tags, and inhibiting irrelevant literal features at the same time. The Chinese text classification method is remarkably superior to a traditional model method in tasks such as metaphor sentiment classification and poetry theme classification, and the accuracy and robustness of Chinese text classification are effectively improved.

The invention provides an SQL generation method and system combining GraphRAG and a large model in the technical field of natural language processing and artificial intelligence crossing, and the method comprises the steps: S1, obtaining an input natural language query statement, and recognizing the user intention of the natural language query statement through a TextCNN text classification model; s2, on the basis of the user intention, matching similar indexes of the natural language query statement through a text sliding block similarity algorithm; s3, querying associated table fields from a preset data knowledge vector library on the basis of the similar indexes through a GraphRAG technology, and filtering each table field; s4, through a preset cue word template, generating a query cue word based on the natural language query statement and the filtered table field; and S5, inputting the query prompt word into a large model to obtain an SQL statement corresponding to the natural language query statement. The method has the advantage that the SQL generation accuracy and efficiency are greatly improved.

Disclosed herein, is a method and system for selecting a LLM for response generation to user queries. The method includes receiving a user query from a user device. The method includes determining, for the user query, a query type from a set of query types through a fine-tuned text classification model. The method includes retrieving a plurality of document embeddings based on the user query and the query type from a vector database through a semantic search technique. The method includes preparing a prompt using the user query and the plurality of document embeddings. The method includes inputting the prompt to an LLM selected from a set of LLMs based on the query type. The method includes generating, via the selected LLM, a response to the user query based on the prompt.

To efficiently and effectively classify a large amount of document and text data by an LLM.SOLUTION: The present invention relates to a text classification system 1 which classifies documents accumulated in a document DB 14 by categories, and the text classification system has a classification processing part 12 which lets an LLM 2 proposes one or more categories based upon a classification policy specified by a user, a search processing part 13 which searches the document DB 14 for documents belonging to the respective proposed categories, and a UI processing part 11 which presents the respective categories and the numbers of documents belonging to the respective categories to the user.SELECTED DRAWING: Figure 1

The embodiment of the invention discloses a short text classification method based on knowledge enhancement prompt learning and a related device, and the method comprises the steps: carrying out the extension of short text information through a large language model, obtaining a rich context information text, enabling an extended information text to be the same as the short text in semantics, and enabling the extended information text to comprise the context information text; performing concept retrieval by utilizing the extended information text and a preset open knowledge graph to obtain a tag word set; based on the tag word set and a preset category tag, constructing a tag word mapper, the tag word mapper comprising a mapping relationship between the category tag and the tag word; and inputting the label word mapper and the short text into a pre-training language model, and carrying out text classification through a prompt learning method to obtain a prediction category label of the short text. Through the method, the problems of high ambiguity and feature sparsity of the short text classification task are reduced, and the short text classification accuracy is improved.

The invention relates to a software function point extraction and element identification method and system, equipment and a medium. According to the method, the natural language processing capability of the large language model and the precise classification capability of the text classification model are combined, so that precise extraction and element recognition of function points in the software requirement document can be realized. And by introducing structured text segmentation, an object attribute cleaning strategy, a cue word optimization extraction scheme, a mechanism of secondary supplemental extraction of ILF function points and a correction optimization strategy based on a heuristic rule, the integrity and accuracy of function point extraction and recognition can be remarkably improved, the situation of mistaken extraction or omission is reduced, and the recognition efficiency is improved. And a solid data basis is provided for software scale automatic estimation.

The invention discloses a large model-based text classification method, which comprises the following steps of: 1, setting a task semantic constraint rule, and writing a fixed cue word template; 2, sorting classification labels and adding semantic description information; 3, semantic similarity clustering and confidence allocation are executed, and a confidence multi-granularity label prefix tree is generated; 4, dynamic constraint autoregressive decoding is executed in combination with the confidence multi-granularity label prefix tree and the large language model, and a target classification label is output; 5, constructing a BERT auxiliary discrimination module to carry out confidence mask constraint; step 6, constructing a decoding temperature control parameter and a semantic bias item to perform semantic guidance and category distinguishing control; 7, performing uncertainty self-calibration on the autoregression decoding process of the large language model; and 8, constructing semantic consistency loss between the large language model and the BERT auxiliary discrimination module. According to the method, the stability and the classification efficiency of text classification in a small sample scene are improved.

A method, computer system, and a computer program product for data-free knowledge amalgamation are provided. Multiple pre-trained teacher machine learning models are obtained. Each is trained on a respective different set of training data. Pseudo-data samples that mimic original training data of the teacher models are generated. A block-wise amalgamation with a self-regulative strategy to integrate knowledge from the multiple teacher models is implemented by inputting the pseudo-data samples into the teacher models and into a student machine learning model. The implementing also includes aligning intermediate representations of the student model with a unified representation capturing relevant features from the teacher models.

The invention discloses a software requirement specification document automatic generation method based on a large language model, and the method comprises the steps: text preprocessing: carrying out the category labeling of sentences in software requirement customer opinion data, and dividing a data set into a training set and a verification set; training a demand classifier: classifying texts in the application data set, designing and training a BERT-GCN model, and summarizing different categories of information into different dimensions of software demands; a text multi-classification task: inputting a to-be-processed customer opinion data text into the trained demand classifier for demand text classification, and outputting a classified tagged text; and generating a standard requirement document: inputting the classified tagged text into a large language model, designing a corresponding output prompt template, and guiding the large language model to generate a standardized software requirement specification document. The problems that an existing method for generating the software requirement specification document is low in efficiency and prone to making mistakes are solved.

The invention relates to the technical field of text processing, in particular to a text classification method and system based on semantic analysis, and the method comprises the following steps: segmenting semantic units, constructing a direction change sequence, positioning mutation nodes, generating a consistency section, forming a convergence section, and outputting a classification result. According to the method, a continuous change sequence is formed by constructing a semantic embedding vector and calculating a direction difference, a semantic mutation point can be anchored and divided into sections by combining mutation intensity identification and local jump tracking, and a semantic closed structure and a convergence section are extracted by means of context direction consistency judgment and generic label comparison; precise recognition of a semantic relation chain is realized, semantic jump and conflict starting points can be dynamically sensed, the semantic boundary recognition capability is improved, and the understanding and classification capability of a model on semantic attribution in a complex context is enhanced on the premise of not depending on a fixed dictionary and shallow statistics. The problems that a traditional model is slow in response to an abrupt change structure and weak in semantic convergence recognition are effectively solved.

The invention discloses a text classification method, electronic equipment and a storage medium, and relates to the technical field of data processing, which comprises the steps of improving the input quality through noise filtering and standardization processing in a word segmentation stage, realizing accurate numerical mapping of semantics by means of an embedded matrix in a vector conversion stage, and improving the input quality. The semantic association between word segmentation units is analyzed and quantified into a weight matrix through interactive operation and normalization processing of query vectors and key vectors, value vectors are subjected to weighted fusion through the weight matrix, comprehensive features containing global contexts are obtained, and a result is output through pooling compression and a dichotomy model. By optimizing matrix operation logic and reducing redundant information processing, the technical problems of high calculation complexity, insufficient expandability and insufficient real-time performance caused by dense matrix operation in a large-scale text classification task are solved, and the purposes of improving the semantic comprehension accuracy and improving the text classification efficiency are achieved while the semantic comprehension accuracy is guaranteed. The text classification efficiency is obviously improved, and the applicability of the model in a large-scale scene is enhanced.

A server may automatically determine a classification for document text of an electronic document and display a graphical indication of the classification of document text in a first graphical region of a first graphical user interface. In response to the server receiving an approval of the classification, the server may generate a label for the document text based on the classification and train a machine learning (ML) model using the label and the electronic document. Furthermore, the server may execute the trained ML model for a second electronic document. For at least one field of a form on a webpage, the server may automatically complete a widget embedded in the web page, using the trained ML model display a second graphical indication of text in the second electronic document providing data for the at least one field.

The invention discloses a knowledge base-based field intelligent decision-making method and system, and belongs to the field of equipment operation and inspection. The invention is oriented to the field of equipment operation and inspection, combines advanced technologies and ideas such as layout analysis, named entity recognition, text generation, large language model fine tuning, text classification and target detection, and constructs a knowledge base-based field intelligent decision method and system for efficient processing and decision support of equipment fault information. The method comprises the following steps: constructing and organizing multi-source heterogeneous data, and integrating high-quality information into a maintenance knowledge base; on the basis, generating a self-adaptive text maintenance strategy by utilizing domain knowledge and responding to a dynamic operation context; and finally, fusing image modal data through an anomaly detection method, accurately positioning a fault, and improving the generation quality of a text maintenance strategy. According to the method, a complete process from multi-source heterogeneous data to multi-dimensional strategy generation is realized, key problems in equipment maintenance are effectively solved, and efficiency and accuracy are remarkably improved.

The invention relates to the field of intelligent information processing, in particular to a carbon border adjustment mechanism intelligent auxiliary customs declaration method and system based on natural language processing, and the method comprises the steps: carrying out the real-time translation and key information extraction of CBAM related rule files and unstructured documents; a dynamic CBAM knowledge graph is constructed based on related rule texts, rule updating is monitored in real time through a text classification and event extraction technology, and calculation rules and declaration logic are dynamically adjusted; performing standardization processing on supplier data in different formats, integrating an industry emission factor library, automatically matching a calculation model according to a product type, generating a carbon emission result meeting a CBAM requirement, and mapping the data to a corresponding position of a CBAM declaration form; based on historical declaration data and related rule texts, analysis and compliance risk prediction are carried out by using a natural language processing technology, so that the declaration data are ensured to accord with latest rules, and violation risks caused by rule changes are avoided.

The invention discloses an AI large model-based arrival person screening method and device, a medium and equipment, and belongs to the field of screening, and the method comprises the steps of firstly obtaining screening conditions input by a user, including text content, image data and qualification information requirements, and to-be-screened arrival person data; next, performing word segmentation, keyword extraction and semantic matching on the text content of the person arrival data by utilizing a preset semantic analysis model in combination with BiLSTM, CRF and LDA technologies, and outputting a semantic score; meanwhile, note styles are recognized through the text classification model, logic judgment is conducted, and styles and logic verification scores are obtained. In addition, element detection and style verification are carried out on the image data through the multi-modal recognition model, and image matching scores are output; the qualification scoring module calculates qualification scores according to a preset weight formula. And finally, fusing the multi-dimensional scores to generate a comprehensive score, and carrying out accurate screening on the arriving persons according to the comprehensive score.

The invention relates to the field of customs document text feature recognition, in particular to a deep learning-based customs document text feature recognition method, which comprises the following steps of: preprocessing a real-time customs document text to obtain customs document text features; establishing a customs document text classification analysis model based on deep learning according to the customs document text features; and performing feedback adjustment processing by using the customs document text classification analysis model to obtain a customs document text feature recognition result, and establishing a dynamic threshold judgment system by considering multi-dimensional features such as data types, data capacity, text content, timestamps and historical data at the same time, so that the abnormal document recognition accuracy is improved, and the user experience is improved. Meanwhile, a cross validation mechanism of the data content classification model and the data content analysis model reduces the omission ratio of high-risk receipts in the test, and effectively solves the problem of cold start of training data.

Systems and methods of determining and transmitting a code designation are disclosed. A request for a code designation of an item, and information about the item are received. A first probability distribution is obtained via a trained text classification model from the textual description. A second probability distribution is obtained via a trained image classification model, from the one or more images. A plurality of candidate code designations based on the first probability distribution and the second probability distribution are generated. Each candidate code designation includes a first portion and a second portion of the code designation. Respective probabilities of respective candidate code designations in a first subset of the plurality of candidate code designations are aggregated, from which a selected code designation is transmitted to the requesting system as the code designation associated with the item.

The invention relates to the field of artificial intelligence and natural language processing, in particular to a long text semantic classification method based on deep learning. The method comprises the following steps: performing structured cleaning and sentence boundary recognition on an original text to obtain a sentence sequence; based on the sentence sequence, constructing a semantic fragment; performing coding processing on the semantic fragments to generate a segment-level semantic vector sequence; based on the segment-level semantic vector sequence, generating a global semantic representation vector through a semantic tension driven aggregation algorithm; and based on the global semantic representation vector, designing a semantic flow enhanced classifier to complete text classification. The method solves the problems that when a traditional text classification method is used for processing long texts, semantic incoherence and context information loss are prone to occurring, and particularly when structural semantic mutation exists in the texts, a traditional model often cannot accurately capture semantic changes caused by the mutation; a traditional classifier is often prone to over-fitting of majority classes of samples and neglects recognition of minority classes.

The invention provides a text classification method and a deep learning model training method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning, natural language processing, content security and intelligent search. According to the specific implementation scheme, the method comprises the steps of generating semantic features of an input text by using a shared base network; according to the semantic features and a splicing weight matrix of the plurality of tower networks, determining respective classification results of a plurality of text classification tasks respectively corresponding to the plurality of tower networks, the splicing weight matrix being obtained by splicing weight matrixes of the same processing layer of the plurality of tower networks; and according to respective classification results of the plurality of text classification tasks, determining a target classification result of the input text.

The invention discloses a test case generation system construction method, which comprises the steps of performing data cleaning on a function module part in a system requirement specification to obtain a structured function data set, performing formatting processing on the structured function data set to obtain a structured data test case framework template file, and performing data processing on the structured data test case framework template file. Preprocessing to obtain a structured data basic test case framework data set; and training a model based on Transform-based text classification analysis for the structured data basic test case framework data set. The test case data set is obtained through the method, test case content supplementation is carried out, and the test case with the table format is output. According to the construction method of the test case generation system, the efficiency of compiling the test case can be improved. The invention further discloses a test case generation method.