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Does there exist a compact set of visual topics in form of keyword clusters capable to represent all images visual content within an acceptable error? In this paper, we answer this question by analyzing distribution laws for keywords from image descriptions and comparing with traditional techniques in NLP, thereby
Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled...
In this paper, we studied a speaker independent isolated speaker recognition system for Turkish language by using cross correlation technique. The power spectrumpsilas of each keyword speech for different speakerpsilas determined using the linear predictive coding in order to constitute a feature vectors database that
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
and responses in MOOCs forum to extract keywords of misconceptions for instructors based on Natural Language Processing (NLP) technique. In this study, the researchers mining these misconceptions from over 120 thousands of single Chinese words and 15 thousands of English words in a course. Moreover, we visualize them by
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