42 results about "Keyword extraction" patented technology

This invention discloses a long text matching method combining noise filtering and a divide-and-conquer strategy. The method includes: constructing a long text matching model, which comprises a keyword extraction layer, an association extraction layer, and a filtering layer. The keyword extraction layer extracts keywords from the text; the filtering layer filters noise from the text based on sentence similarity to obtain a denoised text sequence; and the association extraction layer further removes keywords from the denoised text sequence to obtain the remaining associated text. The long text matching model is trained with the optimization objective of minimizing a set overall loss function, which reflects the global matching distribution and combines the keyword and association matching distributions. For the target text, real-time text matching is performed using the trained long text matching model. This invention improves the generalization ability and accuracy of text matching.

A kind of detection method and device of fake APP, terminal, the method includes: determining training data;Keyword extraction is carried out to the brief introduction of each pair of target APP and the brief introduction of suspected APP, and the data vector of each keyword is determined;Determine the weight value of each keyword, and the data vector of each keyword in the target APP is weighted based on the weight value Operation is carried out to obtain target APP data vector, and obtain suspected APP data vector;The vector comparison result of target APP data vector and suspected APP data vector is calculated, the absolute value of the vector comparison result is determined as first data vector, and the square of the vector comparison result is determined as second data vector;Obtain the training text vector of the pair of target APP and suspected APP;Training fake detection model.The present application can enhance the association between keywords in model training phase, reduce the misjudgment rate.

The application provides a floating floor slab impact sound insulation performance optimization method, device, equipment and storage medium, keyword extraction is performed on target building space information input by a user through a natural language processing module, semantic similarity matching is performed in a pre-constructed noise source library, a target noise source spectrum corresponding to the target building space and a sound insulation standard limit value are automatically obtained, then a floating floor slab sound insulation performance target is determined based on spectrum analysis, subsequently, floating plate material parameters and elastic cushion layer parameters are substituted into a floating floor slab sound insulation performance prediction model established based on a finite element method, impact sound insulation performance prediction values under candidate material combinations are quantitatively calculated, finally, a multi-objective optimization model is constructed and solved in combination with user design constraint conditions, and an optimal design scheme of the floating floor slab matched with actual requirements of the target building space is output, so that precise matching and global optimal design of the acoustic requirements of the building space and the construction scheme of the floating floor slab are realized.

This invention relates to a scientific literature clustering method and system based on unsupervised keyword extraction. First, it effectively extracts keywords from scientific literature by comprehensively considering factors such as the occurrence of words in document abstracts and titles, the semantic similarity between words and the documents themselves, and the characteristics of domain keywords. Then, based on the characteristics of Chinese and English, this invention uses different embedding methods to cluster the extracted Chinese and English keywords, thereby achieving effective clustering of Chinese and English scientific literature. This invention considers the importance of words from multiple perspectives, comprehensively considering their occurrence in document abstracts and titles, and uses a method that automatically adjusts the preset keyword length based on domain characteristics to calculate keyword scores, thus incorporating more features of the words. This invention improves upon existing unsupervised keyword extraction algorithms.

A system includes a processor that executes computer executable components stored in a memory. The computer executable components can include a reception component that receives a query. The computer executable components can further include a contextualization component that contextualizes the query by identifying context of the query and summarizing background information of the query. The computer executable components can further include a keyword extraction component that uses a natural language processor to identify keywords of the contextualized query. Additionally, the computer executable components can include a refinement component that generates a new query based at least in part on the summarized background information and the extracted keywords. The computer executable components can further include an execution component that uses a retrieval-augmented generation system to run the new query.

The embodiment of the application provides a log fault detection method and related device, through initial detection of log data based on a lightweight binary classification model, a first detection result is obtained; when the first detection result indicates that the log data is faulty, keyword extraction is performed on the log data to obtain keywords of the log data; the keywords are matched with fault classification nodes in a knowledge base to determine fault classification nodes corresponding to the keywords; the keywords are matched with fault semantic intent nodes under the fault classification nodes in the knowledge base to determine at least one fault semantic intent node corresponding to the keywords; and a second detection result is determined based on a matching degree of the at least one fault semantic intent node and the keywords. In the embodiment of the application, the log data is initially detected based on the lightweight binary classification model, the depth explainability of the detection result is improved in combination with the knowledge base, and the accuracy of log fault detection is effectively improved.

The application relates to the technical field of data security, in particular to a privacy data desensitization method for a scientific and technological achievement transformation platform, which comprises the following steps: collecting scientific and technological achievement data from the scientific and technological achievement transformation platform, and performing word segmentation and keyword extraction on the text content; performing semantic clustering on the keywords based on the feature vectors of the keywords to construct a generalization hierarchical tree; weighting and fusing the sensitivity and rarity of the keywords to obtain data privacy; dividing equal-length intervals according to the index size of the data privacy, and assigning an adaptive K value to each group for generalization by using a K-anonymity algorithm to perform generalization desensitization processing on the keywords in the scientific and technological achievement data. The application aims to ensure the privacy of high-risk sensitive data in the scientific and technological achievement data and avoid excessive generalization and loss of data effectiveness.

The application discloses a kind of automatic driving test scene generation methods considering two-wheel vehicle driver behavior, comprising: obtaining the numerical data and text data of multiple motor vehicle-two-wheel vehicle traffic accident samples;Numerical data constructs classification variable matrix and continuous variable matrix;Traffic accident document set is obtained based on text data;The correlation of classification variable matrix is handled, and the data corresponding to redundant variable is deleted;For traffic accident document set, theme label classification and theme keyword extraction are carried out by GSDMM model, and accident reason classification variable matrix related to two-wheel vehicle driver behavior is generated;The above matrix is integrated to construct accident scene input variable matrix, and its row vector is clustered and analyzed to obtain cluster label matrix and cluster prototype matrix, so as to obtain the scene description corresponding to each traffic accident type, and automatic driving test scene is generated accordingly.The application can automatically generate automatic driving test scene with authenticity and diversity.

The present invention concerns a method for detecting faults in mechanical components by classifying text strings describing issues with said mechanical components by combining deterministic, rule-based keyword extraction algorithm with deep learning models. It uses a Generative AI model to generate synthetic data, especially for underrepresented classes. The system consists of two main components: a probability map that implements a deterministic, rule-based keyword extraction algorithm by focusing on negations and relevant keywords, and a Transformer-based model that handles long-range dependencies in sequential data. The outputs from both models are combined using score-based aggregation, and an ensemble classifier is trained to integrate these outputs, leading to improved accuracy in handling diverse data. The invention also includes a system with modules for data collection, preprocessing, augmentation, training, and evaluation to ensure optimal classification performance.

This invention discloses a privacy-graded data protection method for intelligent detection of big data. In the field of information security technology, the method first segments the original data text into words, extracts high-dimensional semantic features of each word based on a word embedding model and a context-aware encoder, and calculates dynamic sensitivity scores using a pre-trained deep neural network. Based on this, the words are divided into five increasing sensitivity categories. A differentiated privacy budget strategy is adopted, and intensity-adapted Laplacian noise is injected based on a differential privacy mechanism to generate privacy-preserving word features and construct privacy-protected text. This allows for direct input into a big data intelligent detection system to perform intelligent analysis tasks such as named entity recognition, keyword extraction, relation extraction, and sensitive content identification. Accurate detection is achieved while ensuring privacy security, balancing privacy protection strength with data usability. This method is suitable for privacy computing and intelligent analysis needs in large-scale data scenarios.

This invention relates to a multilingual generative retrieval method based on cross-language semantic compression, belonging to the field of information retrieval technology. The invention includes the following steps: constructing a multilingual document retrieval dataset; extracting keywords from multilingual documents from multiple perspectives using a keyword extraction model, and calculating the extracted keywords using semantic similarity to construct a similarity matrix; performing semantic clustering based on the similarity matrix, representing clusters using atomic IDs, and then assigning document identifiers to each multilingual document by the cluster containing the keywords; in the inference stage, after inputting a query, employing a dynamic multi-complement constraint decoding method, gradually narrowing the decoding range of the document identifier in the current step based on the decoding results of previous steps, thereby obtaining the final document identifier. The retrieval capability of this invention is significantly improved compared to other models.

This invention discloses a two-stage hybrid retrieval method and system based on keyword guidance and semantic ranking. The method performs query analysis and keyword extraction on the user's query text to form a query guidance set; it then uses an inverted index for keyword coarse screening to obtain a coarse screening result set of candidate document fragments; adaptive context expansion is performed on the candidate document fragments; semantic vectorization and similarity calculation are performed to obtain semantic similarity scores; dynamic weight fusion is used to re-rank the coarse screening result set; and the ranked candidate document fragments are optimized to output the final retrieval result list. The system includes a keyword extraction module, an inverted index and coarse screening module, a context intelligent expansion module, a semantic ranking module, and a result optimization module. These modules are sequentially connected and work collaboratively to complete the hybrid retrieval process from query input to result output. This method and system improve retrieval accuracy and recall, while also achieving high retrieval efficiency and low resource consumption.

This invention relates to the field of online comment information processing technology, and particularly to a method and system for quantifying and analyzing cultural understanding in overseas comments on cross-cultural communication games. The method includes the following steps: First, search frequency collection and doubt period location: Based on the release time of each background chapter of the game, collect search frequency data related to the full game title and background chapter names. By comparing and identifying search peaks, mark periods with abnormally high search frequencies as high doubt periods. Second, core search keyword extraction and doubt content location: Extract core search keywords within high doubt periods, classify them according to cultural content type, statistically analyze their frequency, and filter high-frequency keywords. Third, secondary data collection and comprehension deviation restoration.

The application provides a text information processing method and device, electronic equipment and a storage medium. The method comprises: obtaining to-be-processed text information; performing word segmentation processing on the to-be-processed text information to determine at least one candidate keyword vector; dynamically adjusting the candidate keyword vector to form a candidate keyword vector set; determining a keyword extraction strategy matched with the to-be-processed text information based on the candidate keyword vector set and a reward value parameter through a deep reinforcement learning network; and extracting the candidate keyword vector set based on the keyword extraction strategy to obtain at least one keyword vector as a keyword of the to-be-processed text information. Thus, the dependence on word granularity in keyword extraction is reduced, the extracted keyword of the to-be-processed text information is suitable for different use scenarios, and the user experience is improved.

The application discloses an information generation method and device, and relates to the technical field of artificial intelligence. A specific implementation manner of the method comprises the following steps: acquiring current service information; inputting the current service information into a pre-trained keyword extraction model to obtain at least one keyword in the current service information; and inputting the current service information and the at least one keyword in the current service information into a pre-trained corpus generation model to obtain at least one simulated response information of the current service information; wherein the simulated response information comprises at least one simulated response information corresponding to each keyword. The implementation manner can improve the generation efficiency, accuracy and diversity of user response information.

The present disclosure provides a method, device, electronic device, computer readable storage medium and computer program product for generating meeting minutes, relates to the field of natural language processing, and particularly relates to the field of speech recognition and keyword extraction. The implementation scheme is: obtaining first conference audio, wherein the first conference audio includes information indicating that a request execution person is requested to execute a to-do item; obtaining second conference audio according to the first conference audio, wherein the second conference audio is associated with the execution person, and the second conference audio includes information indicating whether the execution person commits to execute the to-do item; and in response to determining that the execution person commits to execute the to-do item according to the second conference audio, generating a target meeting minutes based on the first conference audio, wherein the target meeting minutes indicate that the to-do item is executed by the execution person.

The application provides a hypernym-hyponym relation extraction method based on concept definition and data enhancement, which comprises the following steps: extracting concept pairs from natural text by using a keyword extraction technology, constructing concept triples based on the concept pairs and the hypernym-hyponym relations corresponding to the concept pairs, and taking the set of concept triples as a training data set; obtaining concept vectors in each triple in the training data set, offset vectors between the concept vectors, and vectors of concept definitions; constructing a hypernym-hyponym relation prediction model with the training data set as the input and the fused vectors of the offset vectors between the concept vectors, the concept vectors, and the vectors of concept definitions as the output, training the hypernym-hyponym relation prediction model according to the training data set and the fused vectors; obtaining a to-be-predicted concept triple in a test text, inputting the to-be-predicted concept triple into the trained hypernym-hyponym relation prediction model, and predicting whether the to-be-predicted concept triple has a hypernym-hyponym relation according to the output components.

This invention discloses a cold chain logistics document analysis method based on multimodal and dynamic correlation, comprising the following steps: constructing a cold chain logistics document thesaurus and manually annotating the subject keywords of preprocessed cold chain logistics documents; constructing an analysis model, which includes a keyword extraction module, a ranking module, a temporal enhancement module, a business feature module, and a technical feature module based on a latent Dirichlet allocation word vector model; obtaining subject keywords that integrate temporal, business, and technical features based on manually annotated subject keywords; constructing a loss function and optimizing the parameters of the constructed analysis model by using a preset label minimization loss function; and constructing a dynamic correlation analysis between business and technical subjects. This method provides quantitative decision support for prioritizing technology investment, providing supply chain risk warnings, and determining business innovation directions in the cold chain logistics field, significantly improving resource allocation efficiency and policy response speed.

This disclosure relates to the field of text processing technology, and particularly to a keyword extraction method, apparatus, device, storage medium, and computer program. The method includes: inputting text data to be processed into a pre-trained word vector model for vector mapping processing to obtain word vectors; inputting the word vectors into a bidirectional long short-term memory network for global feature extraction to obtain global features, wherein the global features represent the global features corresponding to the sentence; inputting the word vectors into a text convolutional neural network for local feature extraction to obtain local features, wherein the local features represent the local features corresponding to the word; constructing a probability transition matrix based on the global features and the local features, wherein the probability transition matrix is ​​used to calculate the importance score corresponding to the word; and selecting keywords from the words based on the importance score. This method can efficiently and accurately extract keywords from text data.

This invention discloses a keyword extraction method and system based on knowledge graphs and user intent awareness, belonging to the fields of natural language processing and multimodal artificial intelligence. The method includes: parsing multimodal data input by the user through an intent awareness module to identify the user's query intent; dynamically adjusting the keyword extraction strategy based on the intent using a large language model to achieve cross-modal keyword extraction for text, images, and audio; and outputting keyword results highly consistent with the user's intent. The system correspondingly includes a user input layer, an intent parsing layer, a keyword extraction layer, a multimodal data fusion layer, a dynamic adjustment layer, and an output layer. It overcomes the shortcomings of existing keyword extraction technologies in processing multimodal data, understanding user intent, and dynamic response, significantly improving the accuracy, relevance, and personalization of keyword extraction, and is suitable for intelligent retrieval and analysis tasks in engineering fields and other complex information environments.

The application discloses a dangerous chemical substance intelligent question and answer method and system based on CAS structured retrieval, which comprises the following steps: obtaining question data; performing semantic analysis on the question data to generate a structured SQL query instruction; constructing a hierarchical database, performing CAS hierarchical retrieval by using the structured SQL query instruction, and recalling authoritative information of dangerous chemical substances; constructing a constraint prompt word, jointly coding the user question, the authoritative information and the scene by using a large language model, and generating a constrained dangerous chemical substance management and emergency suggestion; and through a semantic analysis mechanism of "keyword extraction, question classification and SQL classification construction", combining a hierarchical retrieval strategy taking CAS number as an anchor point and a large language model assisted normalization CAS query mechanism, stable mapping of diversified names of dangerous chemical substances to unique CAS numbers is realized, retrieval difficulties and generation inaccuracies caused by diversified names of dangerous chemical substances and fuzzy questions are effectively relieved, and the accuracy, stability and traceability of information retrieval and scenario-based suggestion generation of dangerous chemical substances are improved.

This invention discloses a collaborative, batch analysis system and method for innovativeness based on a large model. Leveraging the capabilities of the large model, it performs hierarchical refinement and keyword extraction on user-proposed innovative points, and retrieves search results on a designated information platform. It then performs batch content analysis and relevance comparison between the user's innovative points and the search results to ultimately determine whether the points possess innovativeness. This invention enhances the large model's ability to analyze and compare novelty points by hierarchically layering them, and avoids problems such as frequent "illusions," limited context windows, and easy loss of long-range memory inherent in large models through human-computer collaboration and batch processing workflows. This significantly alleviates repetitive work in manual retrieval and can be directly applied to practical business scenarios such as technology novelty searches and patent novelty searches.

This application discloses a log processing method, apparatus, electronic device, and storage medium, relating to the field of data processing technology. The method includes: acquiring abnormal logs and normal logs corresponding to a program; performing word extraction processing on the abnormal logs to obtain multiple candidate keywords; determining the anomaly index of each candidate keyword based on its word frequency in the abnormal logs and its inverse document frequency in the normal logs; and determining the abnormal keyword corresponding to the program from among the multiple candidate keywords based on the anomaly index of each candidate keyword. This application extracts abnormal keywords based on the anomaly index of candidate keywords, thus reducing reliance on manual intervention and effectively improving the efficiency of abnormal keyword extraction.

The application relates to a data catalog retrieval method, an electronic device and a computer readable storage medium. The method is applied to a data catalog platform, and the data catalog platform pre-stores a metadata database, a text library, a vector library and a key-value library. The key-value library stores key-value pair data with a service name as a key and service information other than the service name as a value. The method comprises the following steps: receiving original retrieval information input by a user; calling a large model to perform semantic understanding, keyword extraction and term normalization processing on the original retrieval information, and generating standardized retrieval information; performing field matching retrieval in the metadata database according to the standardized retrieval information; performing full-text retrieval in the text library and vector similarity retrieval in the vector library according to the original retrieval information and the standardized retrieval information; matching the standardized retrieval information with the keys in the key-value library and performing relevance calculation on the values corresponding to the candidate keys; and performing fusion processing on the multi-path retrieval results and outputting candidate service information.

The application discloses a kind of Chinese and foreign language literature keyword intelligent generation method, it is related to keyword extraction technical field.The present application includes the following steps: determining classification dimension and carrying out artificial marking in the Chinese and foreign language literature;Chinese and foreign language literature text data are cleaned, and sample data are obtained;With "keyword" as the class label of Chinese and foreign language literature, neural network with text classification as target is trained;TextRank algorithm and TF-IDF algorithm are used to extract text keyword;The extracted keyword is spliced behind title respectively, as data set;Keyword generation model is trained using data set pair;The output result of keyword generation model is analyzed, and accuracy is judged.The present application extracts Chinese and foreign language literature text keyword by improved TextRank algorithm and TF-IDF algorithm, inputs keyword generation model after keyword and title are fused and optimized, analyzes the output result of keyword generation model, judges accuracy, improves keyword generation method accuracy and coverage.

This invention discloses a device mobility method and system with autonomous navigation, relating to the field of device mobility technology. The key technical points include the following steps: acquiring task text information received by the mobile device; retrieving a set of navigation strategies to be tested that matches the task text information from a navigation strategy database; if the task text information contains a single task objective and corresponding basic scene information, performing semantic segmentation and keyword extraction on the task text information to obtain task semantic feature one; evaluating the scene adaptability of task semantic feature one and the set of navigation strategies to be tested to obtain strategy adaptability value one; if the task text information contains multiple related task objectives and cross-scene switching requirements, analyzing the semantic correlation of each related task objective to obtain task semantic feature two; and evaluating the cross-scene switching time and path constraints to obtain strategy adaptability value two; the effect is to enable the device to complete the mobility task using the optimal path.

This invention discloses an intelligent parcel pickup method and system for express delivery stations based on image recognition, belonging to the field of express logistics service technology. The method includes the following steps: S1, training a waybill recognition model: The waybill area of ​​the express delivery at the station is labeled using the LabelMe labeling tool, and the labeled data is input into a YOLOv8s model for training to obtain a "waybill recognition and positioning image model"; S2, encapsulating a text extraction service: Based on the above waybill positioning model, the image to be recognized is segmented into several waybill areas; S3, semantic understanding and keyword extraction: A QWen3-8B large language model service is deployed through Vllm, utilizing the semantic understanding capabilities of the large model to parse user input; S4, business process scheduling: This invention achieves rapid parcel positioning and visual guidance by integrating image positioning, text recognition, and large language model semantic understanding technologies, thereby improving the efficiency of parcel pickup at the station and enhancing the user experience.

Various techniques for parsing user input JavaScript Object Notation (JSON) are disclosed. In some embodiments, a system, process, and / or computer product for parsing user input JSON includes receiving structured data associated with a Generative Artificial Intelligence application or site (GenAI app / site) backend; processing the structured data using an input parsing model to identify one or more keywords associated with user input; extracting the user input using the one or more identified keywords; determining whether at least a portion of the extracted user input includes content that is to be blocked based on data loss prevention (DLP) policies; and in response to a determination that at least a portion of the extracted user input includes the content that is to be blocked, applying policies to the structured data.