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Lecturer in the discipline of natural language processing in the Department of Computer Science at Columbia University
Professor Daniel Bauer’s research in natural language processing aims to develop systems that can accurately interpret natural language in a multimodal environment and linguistic discourse. Bauer focuses on the semantics of natural language and how to efficiently translate between surface text, syntax, and semantics. His work uses deep, formal representations of language meaning. Such representations can be helpful in applications ranging from machine translation to natural language user interfaces and will eventually lead to more intelligent language processing systems.
Bauer received an undergraduate degree in cognitive science from the University of Osnabrück in 2007 and an MS degree in language science and technology from Saarland University in 2010. He completed a PhD in computer science at Columbia University in 2017.
Bauer’s research concentration is on syntactic and semantic parsing. His work combines machine learning techniques with linguistically inspired formal models of the relationship between text, syntax, and semantics. It focuses on learning such models automatically and efficient algorithms for inferring good semantic interpretations. One particular model uses hyperedge replacement graph grammars to construct meaning representations. Bauer is also interested in language “grounded” in other modalities, such as formal knowledge bases, 3-D scenes, vision, and robots. He investigates how the relationship between language and the objects or concepts it refers to can be modeled and used in NLP systems.