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welcome to our papers presentation this paper is on hierarchical topic mining via joint spherical tree and tax embedding and this work is primarily done by the data mining group at uiuc to analyze and explore a large amount of text corporal mining a set of meaningful topics organized into a hierarchy is intuitively appealing which has a lot of applications including course to find topic understanding corpus summarization in a hierarchical manner and hierarchical text classification along a popular line of framework hierarchical topic models extend the classic ones by modeling the text generated process with a latent hierarchy so that topic structures can be discovered our framework is motivated by the limitations of hierarchical topic models firstly they failed to incorporate useramp;#39;s guidance as unsupervised models hierarchical topic models tend to retrieve the most general and prominent topics from a tax collection via maximum likelihood estimation but these discovered topics m
