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This paper proposes an extended vector space model (VSM), which is called M2VSM (meta keyword-based modified VSM). When conventional VSM is applied to document clustering, it is difficult to adjust the granularity of cluster in terms of topic. In order to solve the problem, M2VSM considers meta keywords such as
Keywords Extraction plays a very important role in the text-mining domain, since the keywords can represent the asserted main point in a document. Based on the term network and deleting actor index, an effective keywords extraction algorithm is proposed to extract high frequent terms as well as important terms with
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
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