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Email spamming causes serious problems in the Internet resulting in a huge waste of resources and attracting high attention from research society. Automatic document classification and keyword-based filtering are two kinds of techniques which have been applied to filter spam emails to achieve satisfactory results
In this paper, we have developed a probabilistic approach using PLSA for the discovery and analysis of contextual keyword relevance based on the distribution of keywords across a training text corpus. We have shown experimentally, the flexibility of this approach in classifying keywords into different domains based on
Due to the exponential growth of available text documents in digital form, it is of great importance to develop techniques for automatic document classification based on the textual contents. Earlier document classification techniques have used keyword-based features and related statistics to achieve good results when
coupling human expertise to the machine learning, does so without sacrificing accuracy. PICCIL uses keywords and keyword rules both to preclassify documents and to assist in the manual process of grouping and reviewing documents. The reviewed documents, in turn, are used to refine the keyword rules iteratively to improve
can be expected to be achieved in a QA system. Sentences are classified according to the content. Each classification is classified into a more detailed field. Important keywords are extracted from the sentences classified into the field. Moreover, the extracted keywords are classified into common and peculiar word for
This paper proposes a new method for document categorization, based on support vector machine (SVM) using a concept vector model (CVM). The traditional document classification usually ignores the semantic relations among the keywords or documents. To effectively solve the semantic problem, the domain ontology is used
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