Customer service information providing method and device, electronic equipment and storage medium

Abstract

The invention provides a customer service information providing method and device, electronic equipment and a storage medium. The method comprises the steps of receiving a Chinese text input by a user; inputting the input Chinese text into a Chinese customer service question-answering model based on a Bi-LSTM (Bidirectional Long Short-Term Memory) model and a CNN (Convolutional Neural Network) model to acquire an answering statement; inputting the input Chinese text into a content extraction and intention classification model based on a Bi-LSTM-CRF (Conditional Random Field) model and an LSTMclassifier to acquire customer intention classification and key information; determining service recommended to a user according to the customer intention classification and the key information; inputting the input Chinese text into a Chinese text emotion analysis model based on the CNN model to acquire a user emotion classification; adjusting the answering statement according to the user emotionclassification; and in combination with the adjusted answering statement and the determined service, providing customer service information to the user. According to the method and device optimizationmodel provided by the invention, the automatic customer service answering is realized.

Description

technical field [0001] The present invention relates to the field of computer application technology, in particular to a method, device, electronic equipment, and storage medium for providing customer service information. Background technique [0002] At present, the mainstream customer service platform is still mainly based on manual services, the labor cost is high, and the requirements for the skills and knowledge reserve of customer service personnel are also high. Although some platforms also provide intelligent chat robot services, they only stay at the initial stage of using traditional natural language processing methods to extract feature words and match similar questions. The maintenance costs are high, and the accuracy and recall rates are relatively low, which cannot meet customer needs. [0003] At the same time, since most customer service platforms only provide a single chat function, they do not provide good data mining services for the increasingly accumulat...

AI Technical Summary

Problems solved by technology

[0002] At present, the mainstream customer service platform is still mainly based on manual services, the labor cost is relatively high, and the requirements for the skills and knowledge reserve of customer service personnel are also high.

Although some platforms also provide intelligent chat robot services, they only stay at the initial stage of using traditional natural language processing methods to extract feature words for similar problem matching. The maintenance costs are high, and the accuracy and recall rates are relatively low, which cannot meet customer needs.

[0003] At the same time, since most customer service platforms only provide a single chat function, they do not provide good data mining services for the increasingly accumulated question-and-answer corpus, and the customer value and commercial interests contained in the data have not been fully reflected. The closed-loop process of service has not been formed

[0004] In addition, it is also necessary to mine the characteristics and intentions of customer sentences. The current named entity recognition method based on Bi-LSTM-CRF (reverse long-short-term memory network and conditional random field) only recognizes named entities in the text, and does not recognize the text. other content information in the , and there is no overall intent to recognize the text

In addition, traditional customer sentiment analysis uses keywords or statistical methods to analyze user sentiment, which has the problem of inaccurate recognition

Images