P&T-Inf: A Result Inference Method for Context-Sensitive Tasks in Crowdsourcing  

作　　者：Zhifang Liao Hao Gu Shichao Zhang Ronghui Mo Yan Zhang 

机构地区：[1]School of Computer Science and Engineering,Central South University,Changsha,410075,China [2]Glasgow Caledonian University,Glasgow,G40BA,UK

出　　处：《Intelligent Automation & Soft Computing》2023年第7期599-618,共20页智能自动化与软计算（英文）

基　　金：supported by the National Social Science Fund of China(Grant No.22BTQ033).

摘　　要：Context-Sensitive Task(CST)is a complex task type in crowdsourc-ing,such as handwriting recognition,route plan,and audio transcription.The current result inference algorithms can perform well in simple crowd-sourcing tasks,but cannot obtain high-quality inference results for CSTs.The conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing,but this method ignores the context correlation among subtasks and reduces the quality of result inference.To solve this problem,we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer(P&T-Inf)for CSTs.Firstly,we screen the candidate results of context-sensitive tasks based on the partially ordered set.If there are parallel candidate sets,the conditional mutual information among subtasks containing context infor-mation in external knowledge(such as Google n-gram corpus,American Contemporary English corpus,etc.)will be calculated.Combined with the tree augmented naive(TAN)Bayes model,the maximum weighted spanning tree is used to model the dependencies among subtasks in each CST.We collect two crowdsourcing datasets of handwriting recognition tasks and audio transcription tasks from the real crowdsourcing platform.The experimental results show that our approach improves the quality of result inference in CSTs and reduces the time cost compared with the latest methods.

关 键 词：Crowdsourcing result inference tree augmented naive Bayes CONTEXT-SENSITIVE 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]