Efficient intelligent customer service method for a large corpus

A technology of intelligent customer service and corpus, applied in the field of intelligent interaction, can solve the problems of different scene migration, high cost, high time consumption, etc., to reduce the amount of calculation, improve performance, reduce time and memory usage.

Pending Publication Date: 2019-04-19
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Problems solved by technology

[0016] The technical problem solved by the present invention is: high cost, high time con...

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  • Efficient intelligent customer service method for a large corpus
  • Efficient intelligent customer service method for a large corpus
  • Efficient intelligent customer service method for a large corpus

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Embodiment Construction

[0042] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0043] The present invention proposes an efficient question-and-answer matching scheme, and the basic idea is to perform multi-level pre-classification on the corpus based on the idea of ​​recursive clustering.

[0044] Without special instructions, the functions and data formats follow the Python style, and the meanings of the functions that appear are as follows:

[0045]

[0046] Specifically, the efficient intelligent c...

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Abstract

The invention relates to an efficient intelligent customer service method for a large corpus. According to the method, the recursion clustering algorithm based on keyword weighting is introduced to pre-classify the corpus, the advantage of lexicon matching is reserved based on keyword weighting, and a similarity calculation mode is adopted, so that the time complexity of similarity calculation isreduced. And meanwhile, the cost expenditure of manual marking and word library construction is avoided, and the complexity and the unsuitability of transfer learning do not exist. The system has goodperformance in the man-machine interaction fields of store automatic after-sale, citizen automatic inquiry, official account automatic reply automatic after-sale questions and answers, citizen cloudautomatic inquiry, social APP automatic chatting and the like.

Description

technical field [0001] The invention relates to a human-computer interaction method such as automatic after-sales in stores, automatic inquiry by citizens, automatic reply to public accounts, etc., and belongs to the field of intelligent interaction technology. Background technique [0002] Existing technologies are mainly divided into two types of models: supervised learning and unsupervised learning. Supervised learning is mainly based on text classification and text generation, while unsupervised learning is mainly based on keyword matching and similarity calculation. The introduction is as follows: [0003] 1. Text classification [0004] It mainly revolves around machine learning (classification models such as SVM and Logistic) and deep learning (neural networks such as CNN and RNN). By classifying text, query the answers of the corresponding categories in the knowledge base as feedback. [0005] Disadvantages: A large amount of corpus is required, the newly added prob...

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Application Information

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IPC IPC(8): G06Q30/00G06F16/35G06F17/27
CPCG06Q30/01G06F40/289
Inventor 任君翔李光亚陈诚
Owner WONDERS INFORMATION
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