Finding scientific topics

Finding scientific topics.

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a...

Towards Finding Valuable Topics

Enterprises depend on their information workers finding valuable information to be productive. However, existing enterprise search and recommendation systems can exploit few studies on the correlation between information content and information workers’ productivity. In this paper, we combine content, social network and revenue analysis to identify computational metrics for finding valuable inf...

Finding Bursty Topics from Microblogs

Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microbl...

Topics in advanced scientific computation

Finding Topics in Emails: Is LDA enough?

Our research addresses the task of finding topics at the sentence level in email conversations. As an asynchronous collaborative application, email has its own characteristics which differ from written monologues (e.g., text books, news articles) or spoken dialogs (e.g., meetings). Hence, the generative topic models like Latent Dirichlet Allocation (LDA) and its variations, which are successful...