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A document surrogate is usually represented in a list of words. Because not all words in a document reflect its content, it is necessary to select important words from the document that relate to its content. Such important words are called keywords and are selected with a particular equation based on Term Frequency
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
-specific keywords. An automated profiling algorithm is proposed for this purpose, which starts from generic/noisy reviewer profiles extracted using Google Scholar and derives custom conference-centric reviewer and paper profiles. Each reviewer is expert on few sub-topics, whereas the pool of reviewers and the conference may
documents based on keywords, users normally have a more abstract perception what information they require. Semantic gap, which is the disparity between user's request and query results, has been identified as a challenging issue. In this paper, we are interested in scientific document indexing for retrieval. Knowing the
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