Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing
作者机构：SKLSDE LabSchool of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Department of Computer Science and EngineeringHong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information EngineeringZhejiang Gongshang UniversityHangzhou 310018China
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2021年第15卷第4期
页 面：5-16页
核心收录：
学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work was supported partly by National Key Research and Development Program of China(2019YFB1705902) partly by the National Natural Science Foundation of China(Grant Nos.61932007,61972013,61976187,61421003)
主 题：crowdsourcing knowledge acquisition EM algorithm label aggregation
摘 要：Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.
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