Intelligent question-answer method, device and system

Abstract

An embodiment of the invention provides an intelligent question-answer method, device and system. The method includes: receiving question keywords sent by a client, and subjecting the query keywords to word segmentation processing to obtain one or more first terms; based on the first terms, acquiring a set of candidate recommended words matched with the query keywords, wherein the candidate recommended words are words containing the first terms in a preset database; calculating weight of the first terms; based on the weight of the first terms, calculating similarity of the query keywords and the candidate keywords; returning the candidate recommended words and corresponding answer information with the similarity conforming to preset rules to the client. By the method, device and system, result recommendation accuracy in the intelligent question and answer process can be increased.

Description

Technical field [0001] This application relates to the technical field of information recommendation, in particular to an intelligent question answering method, an intelligent question answering device, and an intelligent question answering system. Background technique [0002] With the increasing development of Internet services, more and more Internet companies need to provide online customer service (referred to as online customer service) to solve customer problems. Obviously, manual customer service cannot respond to a large number of user question and answer requests. This requires an intelligent question and answer system to solve user requests through automatic responses. Intelligent question answering system is a system designed to solve the user's question and answer needs. The bottom layer of the system is a sorting system, which recommends more appropriate answers to users by understanding the user's questions. [0003] The basic process of the existing intelligent que...

AI Technical Summary

Problems solved by technology

[0011] (1) Method 1 cannot effectively distinguish different word segmentation terms, so that the similarity calculation results are not sufficiently differentiated, and the actual application effect is not ideal;

[0012] (2) The method of manually setting weights in Method 2 is inconvenient for system tuning. It takes multiple rounds of iterations to find the appropriate weight setting rules, and the construction cost is high;

[0013] (3) In method 2, different weights are set for different types of Term. This weight setting requires manual prior knowledge and cannot be adjusted with the evolution of the system, resulting in high maintenance costs.

[0014] The above three defects may affect the calculation results of the similarity, and then affect the final recommendation effect of the intelligent question answering system.

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