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Keyword query applies to the database which accommodate structured data provide a search option over text attributes that uses a probability based ranking technique but query facing the issue of poor quality results. The keyword matches with multiple entities because the user does not provide exact data from which we
were used as case studies. The textual contents of the marking schemes were transcripted into electronic documents using same file format as the students' answers. The documents were pre-processed for stopwords removal and each keyword stemmed to address morphological variations. N-gram terms (N=2, 3) were then
keyword and documents, and search the versioned objects that are consistent in the top-k results throughout a given query interval. Finally, we use data from Wikipedia to demonstrate the efficiency and performance of our algorithm.
always ignores relativity of the topic. These affect the topic discovery and topic trend. Therefore, combining with the keywords combination and Word2Vec model to strength expression of semantic information in topic clustering, this article sets weighted K-means algorithm for topic discovery. The results show our weighted K
constraints. Based on the optimal algorithm, which is the notion of a trade-off revealing LP, this paper remains the competitive ratio as 1–1/e with an advertiser credibility factor. During the ranking in the keywords auctions, CTR(Click Through Rate) and credibility factor are added to the trade-off function. In the long
Based on the research and analysis of interactive text properties, the word frequency statistics and synonyms merger are imported to obtain the keywords of interactive text. The Sentence similarity is used to describe the degree of coupling between sentences. Then a novel topic partition algorithm based on average
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