Deep Processing Techniques for Natural Language Processing

UW Professional & Continuing Education

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  • from 9 weeks
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UW Professional & Continuing Education

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About the course

Deep linguistic processing aims to extract meaning from natural language text in machine readable form. Deep linguistic processing is useful in applications that require precise identification of the relationships between entities and/or the precise meaning of the author, such as automated customer service response and machine reading for expert systems. Deep linguistic processing is also essential to the creation of natural language dialogue systems, which allow computers to understand and reply in natural language.

This course covers algorithms for using precision grammars to associate deep or elaborated linguistic structures with naturally occurring linguistic data (parsing) and to associate natural language strings with input semantic representations (generation). It also covers associated techniques for disambiguation (parse, generated string) and transfer (for symbolic machine translation).

Experts

Gina Levow

Gina-Anne Levow joined the UW faculty in 2010 as an Assistant Professor in the Professional Master's in Computational Linguistics Program at the University of Washington. Levow's primary interest is spoken language processing, with a focus on the role of intonation in speech understanding. In Ja...

Shane Steinert-Threlkeld

Research Journal Articles Referential and General Calls in Primate Semantics Shane Steinert-Threlkeld, Philippe Schlenker, Emmanuel Chemla, Linguistics and Philosophy, forthcoming. Ease of Learning Explains Semantic Universals Shane Steinert-Threlkeld and Jakub Szymanik, Cognition, vol 195, no....

Deep Processing Techniques for Natural Language Processing at UW Professional & Continuing Education

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