Unsupervised Models for Unlocking Language Data

(CSE Colloquium Lecture Series)

bio_taylor.jpg
Taylor Berg-Kirkpatrick

Taylor Berg-Kirkpatrick
Current Affiliation: Carnegie Mellon University
Monday,  March 5, 2018 @ 11am
Room 1242, CSE Building
Faculty host: Lawrence Saul

Unsupervised Models for Unlocking Language Data

Abstract: One way to provide deeper insight into data is to reason about the underlying causal process that produced it. I'll present model-based approaches for discovering and analyzing language data that incorporate rich causal structure in novel ways. First, I'll describe a new approach to discovering syntactic structure in raw and unstructured text data by jointly modeling discrete and continuous representations of language. Second, I'll describe an approach to historical document recognition that uses a statistical model of the historical printing press to reason about images, and, as a result, is able to decipher historical documents in an unsupervised fashion. I'll hint at how similar approaches can be used for a range of other problems and types of data

Bio: Taylor Berg-Kirkpatrick joined the Language Technologies Institute at Carnegie Mellon University as an Assistant Professor in Fall 2016. Previously, he was a Research Scientist at Semantic Machines Inc. and, before that, completed his Ph.D. in computer science at the University of California, Berkeley. Taylor's research focuses on using machine learning to understand structured human data, including language but also sources like music, document images, and other complex artifacts.