Twitter exhibits several characteristics, including a limited number of features and noisy text information. Extracting valuable information from Twitter has made hot topic detection a challenging task. In this paper, a novel four-stage framework is proposed to improve the performance of topic detection. Data preprocessing is the first stage. Deep learning is then exploited to enrich short text information via image understanding. Next, improved latent Dirichlet allocation is used to optimize the image effective word pairs, which improves the accuracy of the extracted topic words. Finally, both short text and images are integrated for topic detection, in which the corresponding topics are mined based on fuzzy matching of topic words. A large number of experiments show that the proposed framework significantly improves the performance of topic detection and outperforms the selected baseline methods.
Intensity parametrization of the electric dipole transitions within 4f2 configuration of Pr3+ in SrAl12O19 are reported, in which the mixing of the 4f2 transitional states with the explicit 4f5d states is taken into account. A new set of phenomenological intensity parameters, Tkq, are put forward, which are obtained from a best fit of the calculated and measured relative intensities of transitions from the 3P0 level to the lower J multiplets. The fitted values, T33=3.26×10-5 and T53=-5.46×10-5, have been used to analyze the transitions originating from the 1S0 level. The present results are compared with that of the Judd-Ofelt analysis and the experimental measurements.
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