Beyond Topic: Multi-dimensional Topic Models of Text
Assistant Research Professor in Computer Science
Johns Hopkins University
Friday, February 14, 2014|
12:30pm - 1:30pm
2733 Beyster Building
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About the Event
Over the past decade research and applications of topic models have exploded, paving the way for new types of analyses of text data. Topic models discover latent concepts and themes in text corpora relying on unsupervised learning, allowing applications to a diverse set of corpora. However, topic is only one aspect of many that can influence text and data analysts are often interested in others: sentiment, perspective, aspect, etc.
Mark Dredze is an Assistant Research Professor in Computer Science at Johns Hopkins University and a research scientist at the Human Language Technology Center of Excellence. He is also affiliated with the Center for Language and Speech Processing and the Center for Population Health Information Technology. His research in natural language processing and machine learning has focused on graphical models, semi-supervised learning, information extraction, large-scale learning, and speech processing. His recent work includes health information applications, including information extraction from social media, biomedical and clinical texts. He obtained his PhD from the University of Pennsylvania in 2009.
Open to: Public