聚类句子,以发现事件从多个新闻文章使用铅弹和分馏

D. Saravanapriya, Dr M Karthikeyan
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引用次数: 1

摘要

句子聚类是基于一个文档或一组文档中句子中的关键术语执行的。在同一篇文档中,一个句子可能会出现在不同的主题下,并且具有不同的相似含义的词,使用分层聚类方法无法正确聚类。分层聚类方法具有鲁棒性。它们不是很有效,因为它的时间复杂度是O (n2)。为了克服这个问题,使用了K-means类型的算法,但它只处理很少的文档。提出了一种分层聚类和分区聚类交替使用的算法。它提高了准确性,降低了多篇新闻文章的时间复杂度。它用于对引用同一事件的多篇新闻文章中的文本范围进行分组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering sentences to discover events from multiple news articles using Buckshot and Fractionation
Sentence Clustering is performed based on the key terms in sentences within a document or group of documents. A sentence may come under different topics in a single document with different word of similar meaning which will not be clustered correctly by using hierarchical clustering methods. Hierarchical clustering methods are robust. They are not very efficient as its time complexity is O (n2). To overcome this problem, K-means type algorithms are used, but it handles only few documents. A proposed algorithm uses both hierarchical and partitional clustering method alternatively. It increases the accuracy and reduces the time complexity for multiple news articles. It is applied to group the text spans from multiple news articles that refer to the same event.
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