Natural Language Processing in the Wild: Opportunities & Challenges

Friday, September 7, 2018, 3:00 pm - 4:00 pm PDTiCal
11th Floor Large Conference Room [1135]
This event is open to the public.
NL Seminar
Vivek Srikumar (The University of Utah)
Video Recording:

Abstract: Natural language processing (NLP) sees potential applicability in a broad array of user-facing applications. To realize this potential, however, we need to address several challenges related to representations, data availability and scalability. In this talk, I will discuss these concerns and how we may overcome them. First, as a motivating example of NLP's broad reach, I will present our recent work on using language technology to improve mental health treatment. Then, I will focus on some of the challenges that need to be addressed, with a specific focus on scalability. The motivating question is: How can we systematically speed up the entire NLP pipeline without sacrificing accuracy? As two concrete answers to this question, I will describe our recent results that show techniques for rethinking feature extraction and inference to make trained classifiers significantly faster.

Bio: Vivek Srikumar is an assistant professor in the School of Computing at the University of Utah. He obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2013 and was a post-doctoral scholar at Stanford University. His research lies in the areas of natural learning processing and machine learning and has primarily been driven by questions arising from the need to learn structured representations of text using little or indirect supervision and to scale NLP to large problems. His work has been published in various AI, NLP and machine learning venues and received the best paper award at EMNLP 2014. His work has been supported by grants and awards from NSF, BSF, Google and Intel.

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