标题：Enhancing Object Distinction Utilizing Probabilistic Topic Model
作者：Zhu, Yumin; Li, Qingzhong; Zhu, Yumin
作者机构：[Zhu, Yumin; Li, Qingzhong] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China.; [Zhu, Yumin] Shandong Univ Finance & Econ, Inst S 更多
会议名称：International Conference on Cloud Computing and Big Data (CLOUDCOM-ASIA)
会议日期：DEC 16-18, 2013
来源：2013 INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA (CLOUDCOM-ASIA)
关键词：Latent Dirichlet Allocation; decision tree; context attribute; object; distinction
摘要：We develop a novel method for enhancing object distinction problem, which is to distinct different objects with identical names. The traditional approach is based on the attributes or contexts of the objects, not using the information of some attribute which may propagate between the objects and the attributes. To this end, we propose an approach which uses probabilistic topic model to improve the accuracy. We first model the attribute which has unstructured information and get its topics which may propagate to object as a new attribute; and then calculate the similarity values of the context attributes of the two objects with identical names, then we use these similarity values to build a decision tree model based on the training set. For the problem of object distinction for people, we combine the affiliation similarity with other context attributes similarity to judge whether the two people who share the same name correspond to the same people in real life. Experiments show that our method using Probabilistic Topic Model can achieve high accuracy.