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truth is, it still lacks significant research efforts in the area of Bengali Document Categorization. In the first phase of this paper a model has been designed that extracts keywords from a Bengali document. We crawled over 35000 news documents form popular Bengali newspapers and journals. Those documents have been
by the network -- descriptive keywords, or tags. In this paper we present a model that enables keyword discovery methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid
, keyword extraction and similarity search in the broad fields of text mining, information retrieval, statistical language modeling. In this work, a dataset with 200 abstracts fall under four topics are collected from two different domain journals for tagging journal abstracts. The document models are built using LDA (Latent
features and been put into user document which will be used to infer user's interest. Experimental results indicate that this method gives satisfying user interest and is capable for reality project. This paper also introduce two applications based on user interest detected before: 1) Keywords extraction based on interest (We
between those models, this paper illustrated the deficiencies among those models. Then specifically for heterogeneous information in the web, this paper put forward a semantic representation model, which can statistically analyze latent semantic associations among keywords and semantics in a large training repository of
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