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7217results about "Text database querying" patented technology

Intelligent customer service response method and system

The invention discloses an intelligent customer service response method and system, relates to the technical field of intelligent customer service, and can solve the problems of low response efficiency and poor user experience caused by relying on manual customer service in the prior art. The method comprises the steps of preprocessing the customer service historical chat statements to export customer service corpora, and conducting the word segmentation training on the customer service corpora to obtain a language recognition model; carrying out intention identification on corpora asked by auser to match a corresponding service module, carrying out retrieval analysis in an elasticsearch database corresponding to the service module by utilizing an intention identification result, and summarizing output associated keyword answers to form a pre-selected data set; taking a text formed after word segmentation of the question corpus of the user as an input, and carrying out Elasticsearch retrieval in a pre-selected data set to output candidate keyword answers; converting the segmented keywords and the candidate keyword answers into word vectors respectively, calculating the similaritybetween the segmented keywords and the candidate keyword answers in pairs by using a WMD algorithm, and selecting the keyword answers based on the similarity values to respond to the user.

An information retrieval-based question and answer system and method for knowledge graph energization

The invention discloses an information retrieval-based question and answer system and method for knowledge graph energization, which integrally improve the question and answer effect of the system, expand the user consultation range and improve the question feedback accuracy. According to the technical scheme, the system comprises a knowledge map database for storing domain knowledge map information; a word segmentation and part-of-speech tagging module which segments the user questions and tags the part-of-speech of the user questions; an entity identification and link module which identifiesentities in the user questions and links the entities to nodes in the knowledge graph database; an intention understanding module which obtains an intention understanding result of the user problem based on the entity link result and the distributed representation vector; a retrieval module which retrieves a plurality of corresponding question and answer pairs as roughing results according to theinformation in the user questions based on the retrieval data source; a sorting module which is used for resorting the roughing results by utilizing the distributed representation vectors of the entities; and a semantic matching module which scores the reordering result by using the distributed representation vector of the entity and finally outputs an answer.

A text classification method based on a local and global mutual attention mechanism

The invention discloses a text classification method based on a local and global mutual attention mechanism, and the method comprises the following steps: obtaining text data, carrying out the preprocessing of the text data, and expressing text words through a pre-trained word vector; Capturing global long-term dependence of the text sequence by using a long-short-term memory network, and acquiring local semantic features of the text sequence by using a multi-scale convolutional neural network; Taking the global long-term dependency and the local semantic features as input of a local and global mutual attention mechanism to obtain weighted global long-term dependency and weighted local semantic features; Carrying out weighted pooling to obtain a final global representation vector and a final local representation vector; And inputting the vectors to a full connection layer to fuse the global representation vector and the local representation vector, and then inputting the vectors to a classification layer to classify. According to the method, global long-term dependency and local semantic features are captured in parallel, interaction between the two features is learned in an explicit mode, better text global and local feature representation is obtained, and the text classification precision is further improved.

Matrix decomposition cross-model Hash retrieval method on basis of cooperative training

ActiveCN106777318AImprove mutual search performanceImprove mutual search accuracyStill image data retrievalText database queryingMatrix decompositionHat matrix
The invention discloses a cross-model Hash retrieval method on the basis of cooperative training and matrix decomposition. By the aid of the cross-model Hash retrieval method, the similarity between models and the internal similarity of the models can be effectively constrained for unlabeled cross-model data. The cross-model Hash retrieval method includes implementation steps of acquiring original data and carrying out normalization processing on the original data; carrying out cooperative training to obtain constraints between the models; acquiring internal constraints of the models by the aid of neighbor relations; decomposing training data matrixes and adding the constraints between the models and the internal constraints of the models into the training data matrixes to obtain objective functions; carrying out alternate iteration to obtain expressions of basis matrixes, coefficient matrixes and projection matrixes; carrying out quantization to obtain Hash codes of training data sets and test data sets; computing the Hamming distances between every two Hash codes of the data sets; sorting the Hamming distances to obtain retrieval results. The cross-model Hash retrieval method has the advantages that constraints on the similarity between the models of the cross-model data can be obtained by the aid of cooperative training processes, accordingly, the image and text mutual retrieval performance can be improved, and the cross-model Hash retrieval method can be used for picture and text mutual search service of mobile equipment, internets of things and electronic commerce.

Knowledge graph driving type law intelligent consultation system

The invention provides a knowledge graph driving type legal intelligent consultation system. The knowledge graph driving type legal intelligent consultation system comprises a knowledge construction module used for constructing a question and answer corpus and a legal knowledge base; a legal language understanding module used for training to obtain a corresponding entity recognition model, an intention attribute recognition model and a question answer pair matching recognition model; a feature recognition module used for recognizing legal features; a knowledge association module used for searching attributes associated with the questions; a query matching module used for querying and matching answers in a knowledge base and forming a system question and answer log; and a knowledge operation module used for screening out effective questions in the system question and answer logs and updating answers matched with the effective questions into a knowledge base. According to the system andthe method, the user can be intelligently answered and guided to solve legal knowledge problems, the cost of legal consultation of common people is saved, manpower and material resources are saved, and the working efficiency of the user and legal professionals is improved.
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