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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
In structured peer-to-peer (P2P) overlay networks, similar documents are randomly distributed over peers with their data identifiers consistently hashed, which makes complex search challenging. Current state-of-the-art complex query approaches in structured P2P systems are mainly based on inverted list intersection. When the identifiers are distributed among peers, a complex query may involve many...
After analyzing the disadvantages of traditional text clustering method based on keywords set, a novel approach for clustering of Chinese text based on concept hierarchy is presented. It introduces a Chinese topic classify dictionary as background knowledge to clustering of Chinese text. It adopts a hierarchical
a kernel-selected algorithm based on the lowest similarity, afterwards we get the appropriate keywords to label the topic of each cluster. Finally, experiments on 20Newsgruops email dataset show the validity of our approach and the experimental results also well match the labeled human clustering result.
the EHM vector space model FCM clustering algorithm is proposed to deal with the problems in this paper. By introducing Ecommerce hierachical model (EHM), the automatic constructing concept (ACC) algorithm is proposed at first. Through the ACC algorithm and fields keywords table, the e-commerce concept objects are
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