580 results about "Text categorization" patented technology

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.

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 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 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 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.

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 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 multi-round cross validation and weak supervision noise cleaning method based on a large language model. According to the method, weak supervised learning and large language model reasoning capability are fused, and multi-round cleaning and label optimization are performed on a low-confidence sample by introducing a small amount of high-quality label seeds and combining automatic rule construction and cross validation processes. According to the method, a verification and feedback system with a large language model as a core is constructed, efficient purification and enhancement of weak tags in large-scale text data are achieved, and high-quality training data and intelligent tag optimization support are provided for natural language processing tasks such as relation extraction, text classification and entity recognition.

The invention provides a concept-based self-interpretation text classification method and device, and the method comprises the steps: carrying out the text coding of a target text, and obtaining a coding feature; constructing a concept extractor model based on a competitive attention mechanism, extracting concept features by taking the coding features as input, and obtaining a concept attention score of the target text; obtaining a concept intensity feature of the target text based on the concept attention score; and constructing a text classifier model, and performing text classification on the target text through the text classifier model based on the concept strength features. According to the unsupervised text classification method capable of being explained in the process, semantic concepts are automatically extracted by utilizing a competitive attention mechanism, meanwhile, an evaluation framework is designed, the understandability of the extracted concepts is evaluated by utilizing the capability of a large language model, iterative fine tuning is performed on a network by utilizing feedback provided by the large language model, and the classification efficiency is improved. Therefore, the extracted concept is easy to understand by human beings.

The invention discloses a COPD patient re-admission prediction method and system based on a multi-modal sparse attention mechanism, and the method comprises the steps: S1, obtaining the sample data of a patient, and carrying out the preprocessing; s2, performing time sequence feature extraction by adopting a long-short-term memory model based on an attention mechanism to obtain a time sequence feature vector; s3, performing text feature extraction on the clinical diagnosis data by adopting a text classification model based on a sparse self-attention mechanism; s4, carrying out feature extraction on the preprocessed doctor text data by adopting an improved Transform model; and S5, based on a self-attention mechanism, distributing attention weights for the hidden layer representation vectors of the time sequence feature vector, the clinical diagnosis data and the doctor text data, and inputting the attention weights into a full connection layer for output prediction to obtain a final prediction result. According to the method, different types of data of COPD patients are acquired, correlation degree analysis and multi-modal data feature fusion are performed in combination with an attention mechanism, a time sequence and semantic analysis, and the accuracy and efficiency of a disease prediction model are improved.

A public opinion analysis visualization platform construction method based on sentiment analysis belongs to the technical field of natural language processing, and specifically comprises the following steps: 1, collecting related corpora and data required by sentiment analysis; 2, preprocessing comment data required by sentiment analysis; and filtering invalid texts, and performing sentence segmentation processing. 3, firstly performing text classification on the processed data, then performing sentiment analysis, judging sentiment tendency in a comment text, and finally performing visual display; and 4, performing public opinion early warning based on an obtained sentiment analysis result and a popularity data analysis result. And 5, generating an independent public opinion evaluation report and a summary report through a related API interface of the local large language model. Through the method, the public opinion response efficiency can be remarkably improved, the risk identification capability is enhanced, the complex language understanding bottleneck is broken through, and meanwhile the manual analysis cost is reduced.

The invention discloses a news industry classification method and device based on large language model active learning, relates to the technical field of text classification, and can remarkably reduce the manual annotation cost while ensuring the news text classification precision. According to the scheme, the method comprises the following steps: calling a large language model to classify each news text for multiple times for a plurality of news texts to obtain at least one corresponding tag; based on a majority voting mode, dividing the plurality of news texts and the corresponding labels into high-confidence samples and low-confidence samples; performing label labeling on the low-confidence sample to obtain a labeled sample; and taking the labeled sample and the high-confidence sample as a training set, carrying out iterative training on the classification model by using the training set until the model precision of the classification model meets a preset condition, obtaining a target classification model, and carrying out prediction classification on the news text by using the target classification model.

The invention relates to a text classification method and device, electronic equipment and a readable medium. The method comprises the following steps: acquiring an input text, and classifying the input text through a text classification model to obtain an initial classification result; wherein the text classification model is a BERT model, the initial classification result comprises N candidate tags, each candidate tag is used for representing the category to which the input text belongs, and N is a positive integer; adopting the input text and the N candidate tags to construct a prompt word text; evaluating each candidate tag according to the cue word text through a large language model to obtain a target confidence coefficient of each candidate tag; and based on the target confidence of each candidate tag, selecting the target candidate tag with the highest target confidence from the N candidate tags as a target classification result. According to the scheme provided by the invention, the actual demand of the user can be accurately identified in a complex language scene, so that the accuracy and reliability of text classification are effectively improved.

The invention provides an industry vertical data fine tuning method and device based on a large model, and the method comprises the steps: obtaining an operation order text data flow, carrying out the preliminary structural processing of each operation order text, extracting an operation action, an execution object and a constraint condition, and forming text data carrying an initial label; determining an operation process directed acyclic graph and an emergency operation process network according to the fine tuning corpus of the text classification model, and taking the operation process directed acyclic graph and the emergency operation process network as structured training samples for fine tuning of the text classification model; determining a mixed training batch of fine tuning of the text classification model according to the structured training sample of fine tuning of the text classification model, and setting a dynamic balance threshold value of the standard operation sample and the emergency operation sample; and after fine tuning of the text classification model is completed and deployment is carried out, if the operation ticket text to be examined belongs to emergency operation, parallel or conditional branch logic reasoning processing is carried out based on the structure of the emergency operation process network, and a structured examination report is output.

The invention relates to the field of automatic generation of documents, in particular to an intelligent legal document automatic generation system which comprises a user interaction module, a legal knowledge graph module, a document generation module, a verification module and a database. The user interaction module extracts entities through a named entity recognition algorithm and a filling groove algorithm, detects input defects and outputs a first intention of a user; the legal knowledge graph module uses an improved clustering algorithm and a knowledge graph construction algorithm to classify legal texts and construct a graph; the document generation module matches the template through a longest common subsequence algorithm, and fills the associated content of the knowledge graph into the slot position in combination with a support vector machine; the verification module verifies the data logic and the slot coverage; automatic processing from input to generation of the legal instruments is achieved through multi-module cooperation, the efficiency and accuracy of instrument generation are improved, it is ensured that the content is compliant and the logic is rigorous, and the method is suitable for automatic generation scenes of various legal instruments.

The application discloses a multi-label text classification logistics supplier recommendation method, relates to the technical field of network logistics, and comprises the following steps: obtaining a historical text set of manufacturer and supplier information, and pre-training to obtain a training sample set; a text feature map of the training sample set is established based on a bidirectional gate recurrent unit; a text heterogeneous matrix is constructed according to the text feature map; semantic attention is integrated into the text feature map, and the text feature map is labeled to generate a multi-label text feature map; the multi-label text feature map is multiplied by the text heterogeneous matrix to obtain a multi-label text classification model; a graph convolutional neural network is fused with the multi-label text classification model to obtain a multi-label text classification logistics supplier recommendation model based on the graph convolutional neural network, the feature map is classified, and a prediction result is output; and the prediction result is evaluated to obtain an evaluation result. The application solves the problem of low matching efficiency of manufacturers and suppliers.

The embodiment of the invention discloses a merchant door head photo identification method, device and equipment and a medium, and relates to the technical field of door head photo identification. The method comprises the following steps: acquiring a target door head picture of a merchant, and inputting the target door head picture into a door head segmentation model to obtain a door head region; performing text extraction on the door head region to obtain a text set, and determining a text position and text size information corresponding to each text in the text set; inputting the text set into a text classification model to obtain a classification result of each text; the classification result reflects whether each text is a merchant name; determining a target text in each text of the text set according to the classification result of each text, the text position and the text size information; the content of the target text is a merchant name. According to the technical scheme, the effect of automatically recognizing the merchant name is achieved, the recognition efficiency is greatly improved compared with a manual labeling mode, and the recognition accuracy is greatly improved compared with an OCR recognition mode.

The invention provides a multimedia resource content risk identification method and electronic equipment. The method is applied to a content risk identification system comprising a multi-modal dialogue large model, a text classification model and a decision center, and comprises the following steps: the decision center generates a first-level content description problem for multimedia resources, and the multi-modal dialogue large model generates a first-level content description text for answering the problem, the text classification model outputs a first-level risk probability value of the multimedia resource based on the text; under the condition that the ith-level risk probability value does not meet the recognition stop condition, the decision center generates an (i + 1) th-level content description problem, the multi-modal dialogue large model generates an (i + 1) th-level content description text used for answering the problem, the text classification model outputs the (i + 1) th-level risk probability value of the multimedia resources according to the text, and i is a positive integer; and determining a content risk identification result of the multimedia resource based on the ith-level risk probability value under the condition that the ith-level risk probability value meets an identification stop condition.

The invention provides a text classification method and device, electronic equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that a training sample set is obtained, and the training sample set comprises sample texts and sample classification labels and sample reasoning reasons corresponding to the sample texts; performing fine tuning on a first pre-trained large language model through the training sample set to obtain a text classification model; in response to a text classification request, through the text classification model, based on a preset reasoning constraint parameter, only performing classification processing on a to-be-classified text to obtain a prediction classification label; wherein the preset reasoning constraint parameters comprise an output length limiting parameter and a logit probability intervention parameter. According to the method, the text classification efficiency can be improved while the accuracy and the reliability of a text classification result are improved.

The invention provides a cross-language text classification and processing method and system based on deep transfer learning, and relates to the technical field of text processing, and the method comprises the steps: extracting feature representations of a source language text and a target language text at different linguistic levels through a multi-level semantic transfer network; and determining an optimal alignment path, performing nonlinear mapping alignment to obtain fusion features, propagating category semantics by using a semantic bridging function, iteratively updating pseudo-tag confidence distribution of the target language text, and completing classification in combination with a multi-task learning model. According to the method, the problem of text classification in a cross-language scene is effectively solved, and the accuracy and efficiency of low-resource language text processing are improved.

The invention provides a stability and bug testing method after updating of a game operation system, and belongs to the technical field of game stability and bug testing. S2, performance anomaly detection and collapse prediction; s3, Bug text classification is carried out; s4, generating an intelligent test case; s5, carrying out performance optimization related detection; s6, defect and anomaly analysis; and S7, performing test execution and closed-loop optimization. According to the invention, the stability and the accuracy and effect of bug testing are improved.

The invention discloses a standard file automatic classification method based on artificial intelligence, and relates to the technical field of text classification, and the method comprises the steps: obtaining original data of a to-be-classified file, and carrying out the analysis and preprocessing of the original data of the to-be-classified file, and obtaining metadata, chapter structure information and plain text content; inputting the plain text content and the chapter structure information into a multi-granularity semantic pyramid model for analysis and fusion, and generating a document feature vector; constructing a standard classification system knowledge graph by using the standard classification system data to obtain a classification name mapping table, and performing reinforcement learning on the standard classification system knowledge graph by using a graph neural network technology to generate an enhanced feature vector; and calculating the semantic similarity between the document feature vector and the enhanced feature vector. According to the method, hierarchical reasoning is performed in the knowledge graph based on the similarity to obtain the target classification code, and the classification result is matched and output through the mapping table.

The invention provides a text quality inspection method, apparatus and device, and a computer readable storage medium. The method comprises the steps of obtaining a text to be subjected to quality inspection; based on a dictionary matching network in the text quality inspection model, determining a first sensitive keyword matched with a target sensitive word in a target dictionary in the text to be subjected to quality inspection; determining a classification label of the text to be subjected to quality inspection based on a text classification network; under the condition that the classification label of the text to be subjected to quality inspection is a sensitive text, performing semantic recognition processing on the text to be subjected to quality inspection based on a semantic recognition network to determine a second sensitive keyword in the text to be subjected to quality inspection; and outputting quality inspection information comprising the first sensitive keyword and / or the second sensitive keyword. According to the method and the device, the text quality inspection accuracy is improved, and the text quality inspection cost is reduced. And through a text quality inspection technology, the situation that a text containing too many sensitive words is used for training the financial or medical service intelligent customer service model is avoided, so that the compliance of the financial or medical service intelligent customer service model is improved.

The invention relates to the technical field of natural language processing text classification, in particular to a multi-label text classification method based on semantic representation enhancement and dynamic weighted depolarization contrast learning and application of the multi-label text classification method. Preprocessing data in the training data set to obtain input tensor representation; enhancing text semantic representation by fusing multi-layer hidden semantic representation of a depth model; secondly, an improved label graph convolutional network is constructed, regularization, layer normalization, residual connection and label perception attention pooling are introduced into a graph neural network, fine-grained representation of the relation between labels is achieved, and label-text interaction is enhanced; and finally, introducing depolarization weighted contrast loss to construct dynamic weighted depolarization contrast learning, endowing a negative sample with a higher weight, and reducing false negative sample interference, thereby overcoming label semantic overlapping, and aiming at solving the problem of how to enhance the characterization capability of the model in a multi-label semantic overlapping and label incomplete scene.

The invention provides a sample generation method, a text classification model training method and device and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of text classification, natural language processing and deep learning. According to the implementation scheme, a target text unit is recognized from an original sample set used for training a text classification model; determining a target category of an enhanced sample to be generated for the target text unit in the plurality of categories; obtaining a first semantic scene rule corresponding to a target category of the target text unit; and based on the target text unit and the first semantic scene rule, utilizing the large model to generate a first enhanced sample for training a text classification model.