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Jordan B.



College Park, MD

Member since:

September 14, 2013

How are you currently working with NLP?

My research focuses on discovering hidden structure in natural language to create new applications. These applications help users sift through decades of documents, discover when individuals control the topic of a conversation, or to compete against humans in games that are based in natural language. Because data are cheap, I focus on techniques that are unsupervised or only lightly supervised, requiring little annotation or human intervention.

What NLP-related topics would you be interested in learning more about?

Use of topic models and sentiment analysis in industry.

Would you be willing to give a short presentation about your work or research projects at a future meeting? If so, please tell us a little about it.

My research focuses on a class of probabilistic algorithms called topic models that help information consumers navigate large datasets. Given a large collection of documents, topic models discover the constituent themes in a corpus. For example, given a decade's worth of New York Times articles and the number of topics K you want the model to discover, topic models can discover themes related to "business", "technology", and the "arts". I have published research on * techniques to evaluate the output of topic models, * techniques for users to interact with models in a tight loop, * scalable techniques to allow these models to discover new words in streaming data, and * applications of topic modeling to discovering political slant and framing.


I am an Assistant Professor at the University of Maryland iSchool and University of Maryland Institute for Advanced Computer Studies. Previously, I was a graduate student at Princeton with David Blei.

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