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Multiple leaf nodes based parse trees for News Fetching Systems

机译:用于新闻提取系统的基于多个叶节点的解析树

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With the growth of internet people are shifting from their paper based news system to online news system. These online news websites are very useful for the people who have internet access and they can easily read and search their required news through home/office computer or cell phone without wasting their time. To search a particular news, many searching algorithms and News Fetching Systems (NFS) are available which return the results in less than a second. But how much correct information they are giving from a huge data is still a critical area, where we need some efficient algorithms to give us more optimized results. We propose a Multiple Leaf Nodes based Parse Trees for NFS where all the leaf nodes are commonly used alternative words for a specific word. We evaluate our results for a randomly selected webpage and compare with the simple parse tree system. Analysis shows that our proposed algorithm can enhance the performance of any NFS.
机译:随着互联网的发展,人们正在从纸质新闻系统转向在线新闻系统。这些在线新闻网站对于可以访问互联网的人们非常有用,他们可以通过家用/办公计算机或手机轻松阅读和搜索所需新闻,而不会浪费时间。要搜索特定新闻,可以使用许多搜索算法和新闻获取系统(NFS),它们可以在不到一秒钟的时间内返回结果。但是,他们从海量数据中获得多少正确信息仍然是一个关键领域,我们需要一些有效的算法来为我们提供更优化的结果。我们为NFS提出了一个基于多个叶节点的解析树,其中所有叶节点都是特定单词的常用替代单词。我们评估随机选择的网页的结果,并与简单的分析树系统进行比较。分析表明,我们提出的算法可以增强任何NFS的性能。

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