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Rule-based and machine learning approach for event sentence extraction in Indonesian online news articles

机译:基于规则的机器学习方法在印尼在线新闻文章中提取事件句

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With the rapid maturity of internet and web technology over the last decades, the number of Indonesian online news articles is growing rapidly on the web at a pace we never experienced before. In this paper, we introduce a combination of rule-based and machine learning approach to find the sentences that have tropical disease information in them, such as the incidence date and the number of casualty, and we measure its accuracy. Given a set of web pages in tropical disease topic, we first extract the sentences in the pages that match contextual and morphological patterns for a date and number of casualty using a rule-based algorithm. After that, we classify the sentences using Support Vector Machine and collect the sentences that have tropical disease information in them. The results show that the proposed method works well and has good accuracy.
机译:随着互联网和Web技术的快速成熟,在过去几年中,印度尼西亚在线新闻文章的数量在我们以前从未经历过的速度迅速增长。在本文中,我们介绍了规则的基础和机器学习方法的组合,找到了它们中具有热带疾病信息的句子,例如伤亡的发生日期和数量,我们测量其准确性。在热带疾病主题中给出了一组网页,首先使用基于规则的算法来提取与伤亡日期和数量匹配的页面中的句子。之后,我们使用支持向量机分类句子,并收集它们中具有热带病信息的句子。结果表明,所提出的方法运作良好,精度良好。

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