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In this paper, a method of automatic Chinese keyword extraction based on KNN is proposed. Firstly, it preprocesses the document by vector space model. Secondly, it constructs a set of candidate keywords based on KNN method and the labeled dataset. Finally, it post-processes on candidate keywords by the character of
compromised. The encrypted data on the cloud can be retrieved using Searchable Symmetric Encryption (SSE). The current work uses multi-keyword searchable encryption scheme with top-k retrieval to avoid compromises on data privacy occurred by using Order Preserving Encryption schemes. The encryption scheme uses homomorphic
components rather than a single Database table. So to minimise the time constraint, memory space and to do a smart search a new IR system is introduced. In the proposed system, searches can be divided into three categorise, namely (i) Main topic search (ii) Subtitle search and (iii) Keyword search. So the system would search
retrieve the maximal set of relevant and quality page. In our proposed approach, we calculate the unvisited URL score based on its Anchor text relevancy, its description in Google search engine and calculate the similarity score of description with topic keywords, cohesive text similarity with topic keywords and Relevancy
A method is proposed to annotate editorials and news articles for sentences that most accurately represent the opinion of the speaker towards the issue. The speaker's point of view or level of discernment of the issue is whittled out. A list of informative and related keywords is extracted from the document based on
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
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