An Entropy-based Approach to the Crowd Entity Resolution

Yi Jiang, Wei Zhang, Haiyan Zhao
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Abstract

Crowdsourcing is used to obtain needed ideas and content by soliciting data from a large group of people, especially from an online community. However, the data generated by a group of people is duplicated. As to learn the crowd intention based on the crowd data, we need to do some entity resolution works. Previous works focus on data matching and merging, but remain far from perfect in crowdsourcing area. In our study, we propose a generic way in measuring and representing the crowd intention based on the crowd data. The main contribution of our study is twofold: 1. We propose a graph structure that represents the crowd intention. 2. We propose an entropy-based measurement that evaluates the diversity of the crowd intention.
基于熵的群体实体解析方法
众包是指通过向一大群人,尤其是在线社区征求数据,来获得所需的想法和内容。然而,一组人生成的数据是重复的。为了根据人群数据了解人群意图,我们需要做一些实体解析工作。以往的工作主要集中在数据匹配和合并方面,但在众包领域还远远不够完善。在我们的研究中,我们提出了一种基于人群数据的人群意向测量和表示的通用方法。本研究的主要贡献有两个方面:1。我们提出了一个表示人群意图的图结构。2. 我们提出了一种基于熵的测量方法来评估群体意图的多样性。
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