Edinburgh Research Explorer

Dr Shay Cohen

Chancellor's Fellow

Research Interests

My broad interests are in the intersection of computational linguistics and machine learning. I am interested in developing ways for reasoning about compositional structures such as parse trees through the use of formalisms such as probabilistic grammars.

Much of my research has relied on capturing the syntax of natural language using probabilistic grammars -- grammars which originate in linguistics and formal language theory, and which have been augmented with a probabilistic interpretation. I have worked with various grammars, such as probabilistic context-free grammars (PCFGs), latent-variable PCFGs, dependency grammars, adaptor grammars, tree substitution grammars, shift-reduce grammars and others.

Qualifications

BSc Mathematics and Computer Science, 2000, Tel Aviv University, School of
Exact Sciences
MSc Computer Science, 2004, Tel Aviv University, School of Exact Sciences
PhD Language and Information Technologies, 2011, Carnegie Mellon
University, School of Computer Science

Biography

Shay Cohen is a Chancellor's fellow (assistant professor) at the University of Edinburgh (School of Informatics). Before that, he was a postdoctoral research scientist in the Department of Computer Science at Columbia University, and held an NFS/CRA Computing Innovation Fellowship. He received his B.Sc. and M.Sc. from Tel Aviv University in 2000 and 2004, and his Ph.D. from Carnegie Mellon University in 2011. His research interests span a range of topics in natural language processing and machine learning, with a focus on structured prediction. He is especially interested in developing efficient and scalable parsing algorithms as well as learning algorithms for probabilistic grammars.

Research outputs

  1. Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication

    Research output: Contribution to journalArticle

  2. The SUMMA Platform Prototype

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. An Incremental Parser for Abstract Meaning Representation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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