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Multi-label categorizing local event information from micro-blogs

机译:多标签从微博中分类本地事件信息

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Micro-blog service Twitter holds innumerable userposted short messages called tweets that cover various topics including local events. We proposed a method to extract a mount of various local event information using natural language processing from Twitter. This paper describes a method to extract event information and label categories such as music or culture to them. Our proposal is composed of two steps: 1) extract local event information from tweets related to local event by the Support Vector Machine and Conditional Random Fields approach. 2) label categories by combining the output from classifiers of each event category. We implement the proposed method in three ways that consist of keyword matching designed by hand, machine learning and hybrid of them. Besides, we evaluate classification performance using typical five kinds of event categories. As a result, we confirmed the method of the hybrid has highest average F-score 0.674 in the methods.
机译:Micro-Blog Service Twitter包含名为Tweets的无数Userposted短消息,涵盖各种主题,包括本地事件。我们提出了一种使用来自Twitter的自然语言处理提取各种本地事件信息的方法。本文介绍了一种提取事件信息和标签类别(如音乐或文化)的方法。我们的提案由两个步骤组成:1)通过支持向量机和条件随机字段方法从与本地事件相关的推文中提取本地事件信息。 2)通过将每个事件类别的分类器的输出组合来标记类别。我们以三种方式实现所提出的方法,该方法由手工,机器学习和混合的手动设计的关键字匹配组成。此外,我们使用典型的五种事件类别评估分类性能。结果,我们证实了混合动力车的方法在方法中具有最高的平均F分数0.674。

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