A question labeling method based on deep reinforcement learning for an online question and answer platform

Abstract

The invention discloses a question labelling method based on deep reinforcement learning for an online question and answer platform. The method is based on a deep neural network and a reinforcement learning technology. According to the technical scheme, the method comprises the following steps: firstly, establishing a model, innovatively adding an index for measuring the diversity of problem labels while ensuring the labeling accuracy when designing model rewards, and meanwhile, considering a tail label effect, namely, long labels which are described in a complex and detailed manner during labeling, so that the labels can describe the problems deeply and more detailedly. According to the method, the accuracy and diversity of labels are comprehensively considered, the influence of the taillabel effect on problem labeling is reduced, the training efficiency and accuracy of the reinforcement learning model are improved by introducing the deep neural network, and the matching error rangecan be ensured under a certain confidence degree. According to the scheme provided by the invention, mass questions and labels in the question and answer platform can be accurately and diversely matched.

Description

technical field [0001] The invention belongs to the field of natural language processing, and more specifically relates to a question labeling method based on deep reinforcement learning for an online question answering platform. Background technique [0002] With the development of Web 2.0, the development of social question answering (sQA) websites such as Quora1 and Zhihu is becoming more and more important. On the one hand, similar to community-based question answering (cQA) sites, they include a mechanism for asking questions, a platform for posting answers to questions, and a community built around this information. On the other hand, the sQA website highlights social information, especially connections between hashtags. For example, in Zhihu, users must assign at least one hashtag to their questions, and they are able to follow the hashtags they are interested in, which in turn benefits topic-based question routing and browsing. With this in mind, automatically prov...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the existing technical defects, the present invention discloses a new question labeling method based on deep reinforcement learning for online question answering platforms

The invention can effectively solve the overfitting problem caused by too many labels and few problem samples, and improve the diversity of labeling while considering the accuracy of labeling for the problem

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