Method and device for updating question and answer ranking model for automatic question answering system

Abstract

The invention discloses updating method and device of a question answer sequencing model for an automatic question answer system. The method comprises the steps of acquiring feedback information which is returned by the automatic question answer system and represents the interaction behavior of a user to a question answer set list for user search, wherein the question answer set list includes question answer sets which are of preset quantity and sequenced from high to low according to the matching degree of the user search; creating training samples according to the feedback information; marking positive and negative samples for the training samples, wherein each training sample includes user search and a question answer set; determining a characteristic parameter set of each training sample; creating sequencing training data according to the characteristic parameter set of each training sample and the positive and negative examples marked for each training sample; updating the question answer sequencing model according to the created sequencing training data. With the adoption of the method and device, the question answer sequencing model can be automatically updated by the machine learning method based on the user feedback information, so as to adapt to meet the user demand, as well as increasing the experience of the user in the automatic question answer system.

Description

technical field [0001] The invention relates to the field of automatic question answering, in particular to a method and device for updating a question answering sorting model for an automatic question answering system. Background technique [0002] A question answering system is a system that can answer questions automatically. The system can calculate various features between the question entered by the user and the candidate question-answer pairs in the question-answer library, such as word frequency, reverse word frequency, space vector model, and so on. Then, using the question-answer ranking model, according to the calculated features and the weights of each feature, the matching degree between the question and the candidate answer pair is obtained, and then the candidate question-answer pair is sorted according to the matching degree, and the ranking is ranked by The previous question-answer pairs are returned to the user. [0003] In the existing automatic question...

AI Technical Summary

Problems solved by technology

Once the Q&A ranking model needs to be updated, the developer needs to manually adjust the feature weights, which greatly increases the developer's maintenance workload, and the efficiency is low and the maintenance cost is high

[0004] In addition, the existing Q&A ranking model update mechanism does not introduce user feedback data

As a result, the question-and-answer ranking model cannot be updated in a timely manner according to user needs, and it cannot better fit user intentions.

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