Sentence examples for topics discovered from inspiring English sources

Finally, we visualize the bursty sentiment-aware topics discovered by TUS-LDA.

To summarize, the effectiveness of topic modeling schemes entirely depends on the quality of the latent topics discovered.

Whereas men are talking about "twitter," "beer," "obama ., and "itunes" (these are actual top trending topics discovered by Taykey).

However, several supervised methods [14-16] [14-16]at the topicshowscovered by LDA models are driven by general image regularithat rather topicshe semantic regularities for image annotation.

An analysis of previous supervised LDA models shows that the topics discovered by generative LDA models are driven by general image regularities rather than the semantic regularities for image annotation.

However, an analysis of previous supervised topic models [14] shows that the topics discovered by these models are driven by general image regularities rather than the semantic regularities for image annotation.

The redesign means that HealthTap users can now navigate through a wide range of health topics, discover answers and relevant info by tapping on a dynamic image of the human body, alongside getting access to interactive images, and doctor-created videos aimed at breathing new life into health information.

While pre-defined gene sets on metabolic pathways are probably reasonably well curated and updated (e.g. in the KEGG database), it is unlikely that gene sets are available capturing all genes controlling metabolites measured in the clinic - a topic discovered for our Metabolomics GWAS.

In order to better capture the order relationships between terms, and thus improve the topic discovering effectiveness, this paper classified the order relationships between terms into forward dependence and backward dependence and proposed a feedback recurrent neural network-based topic model.

Assume a set of topics are discovered from documents, topic forecasting intends to predict the future trend (i.e., popularity) of the topics/patterns, by using temporal correlations of the patterns observed from training data.

The PLSA topics are discovered as before in phishGILLNET1 and topic distribution probabilities on training data are estimated.