Method for constructing and processing human behavior text data set based on crowdsourcing

Abstract

The invention discloses a method for constructing and processing a human behavior text data set based on crowdsourcing, which comprises the following steps of: firstly, determining a subject object needing to be collected, generating a task according to a specific requirement, publishing the task to a crowdsourcing platform, and obtaining a text data set of all possible human examples under a set subject; presenting the text of the same behavior or event in a plurality of sentences after being written by different persons, so that different sentences describing the same event need to be clustered together, and different text representations belonging to the same behavior are clustered into one class for the acquired data set by adopting a clustering mode; mining a precedence relation structure existing between behaviors by adopting a correlation analysis technology; adopting the mutual information technology to learn a mutual exclusion relation structure existing between behaviors, creating various relations existing in the human behaviors into a plot. What events can occur under a certain condition is indicated, the occurrence mode of the events is limited, and the analysis accuracy of the human behaviors is improved.

Description

technical field [0001] The present invention relates to the technical field of mobile Internet applications, in particular to a construction and processing method of a crowdsourcing-based human behavior text data set. Background technique [0002] With the rapid development of artificial intelligence, various forms of intelligent agents such as intelligent care robots and self-driving cars are also playing an increasingly important role in human life. However, with the popularization of intelligence, the analysis of human behavior and the process of judgment and decision-making are becoming more and more important. However, the existing design of ethical agents, based on expert examples, inverse reinforcement learning, etc., learns human values ​​​​through human demonstrations, but the collection of required data sets has the following disadvantages: expensive, long cycle, bias and other problems, resulting in data set Not comprehensive enough, therefore, not very accurate ...

AI Technical Summary

Problems solved by technology

However, the existing design of ethical agents, based on expert examples, inverse reinforcement learning, etc., learns human values ​​​​through human demonstrations, but the collection of required data sets has the following disadvantages: expensive, long cycle, bias and other problems, resulting in data set Not comprehensive enough, therefore, not very accurate analysis of human behavior

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