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Boosting Country Classification for Semantic Annotation in Social Networks: Person and Place Country Recognition

机译:促进社交网络中语义注释的国家分类:人和地方国家识别

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Much research has been done on named entity recognition such as whether the name is a person, company or place, and valuable contributions have been made. However, there has been little research on country recognition of people's names and places. In this paper, we develop a classification technique for social multimedia to automatically classify countries for person or place. This technique will be used in location search, recommendation services, advertisements and country evaluations. Based on binary vector space model (VSM) and boosting algorithm ideas, GBBoosting classification algorithm is designed to support country classification. Since the names for different country multimedia content are very similar sometimes, we construct a weak learner to solve this problem. Compared to weighted similarity and Naïve Bayes classification algorithm, GBBoosting classification algorithm is more efficient and has higher recognition rate. GBBoosting classification algorithm has outstanding performance, especially in distinguishing countries with similar spelling.
机译:在命名实体识别方面已经进行了很多研究,例如名称是人,公司还是地点,并且已经做出了宝贵的贡献。但是,关于国家对人们的姓名和位置的认可的研究很少。在本文中,我们开发了一种用于社交多媒体的分类技术,可以对国家或地区进行自动分类。该技术将用于位置搜索,推荐服务,广告和国家评估。基于二进制向量空间模型(VSM)和Boosting算法思想,设计了GBBoosting分类算法以支持国家分类。由于有时不同国家/地区的多媒体内容的名称非常相似,因此我们构造了一个能力较弱的学习者来解决此问题。与加权相似度和朴素贝叶斯分类算法相比,GBBoosting分类算法效率更高,识别率更高。 GBBoosting分类算法具有出色的性能,尤其是在区分相似拼写的国家中。

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