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This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We
detail. The paper presents the similarity algorithm of domain keywords and common words respectively and integrates them into the question similarity. Experimental results show that the proposed method can achieve good performance and the system is applied.
keywords from messages posted on social media which will be helpful in the identification of various communities, category of user and hidden pattern present in the social media. In this paper, we applied Probalistic approach to recognize the new keywords and assign the group accordingly. State-of-the-art studies performed
, irrelevant tweets were further segregated by means of a unigram dictionary containing education-oriented keywords. The Apriori algorithm was then applied to the dataset thus obtained resulting in characteristic markers or patterns of these institutes.
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