Detecting topic evolution in scientific literature: how can citations help?
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"Detecting topic evolution in scient..." refers background or methods in this paper
...Here, we use one of the most popular models in machine learning and information retrieval, the Latent Dirichlet Allocation (LDA) [3] framework, to generate topics....
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...The LDA model on the bag of words [3] was extended to model 1) the impact of authors [29, 33]; 2) the impact of the directionsensitive messages sent between social entities (e....
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...All these hyper parameter settings simply follow the tradition of topic modeling [3]....
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...Since the Latent Dirichlet Allocation (LDA) model [3] has been extensively adopted in information retrieval [4, 12, 25, 29, 33, 18, 22, 24], as the first step we extend LDA for topic evolution analysis....
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25,546 citations
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5,680 citations
"Detecting topic evolution in scient..." refers background or methods in this paper
..., [14]) reported that LDA under Gibbs sampling normally requires around 500-1, 000 iterations to reach convergence....
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...Collapsed Gibbs sampler can be used to infer the LDA posterior probabilities [14]....
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3,755 citations
"Detecting topic evolution in scient..." refers methods in this paper
...It can be easily extended to any dynamic number of topics using algorithms such as Hierarchical Dirichlet Process [30]....
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