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In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it(More)
In this paper, we introduce and study an efficient regular queries processing algorithm on a very large XML data set which is fragmented and stored on different machines. The machines are connected by the high speed interconnection. In this system the efficiency of a query processing algorithm depends on two main factors: the waiting time for the answer and(More)
There are two main topics considered in this paper: (i) Vietnamese words are recognized and sentences are segmented into words by using probabilistic models; (ii) the optimum probabilistic model is constructed by an unsupervised learning iteration. For each probabilistic model, new words are recognized and their syllables are linked together. They are new(More)
Sentiment analysis of online users has been attracting significant interests in both academics and industry, but is always challenging. In this paper, we propose an effective algorithm that can work with text streams and big text collections, without human supervision. This method is based on the state-of-the-art model, namely Aspect and Sentiment(More)