Continuous State Machines and Grammars for Linguistic Structure Prediction

Continuous State Machines and Grammars for Linguistic Structure Prediction

Explore continuous state machines and grammars for linguistic structure prediction in this lecture from the Simons Institute. Delve into dependency parsing, recurrent neural networks, and stack LSTM parsers. Learn about global and greedy parsing paradigms, token and tree representations, and multilingual parsing approaches. Examine variations like adding semantics to parsers and using RNN grammars. Investigate phrase-structure trees and techniques for tree and string generation. Gain insights into cutting-edge research in natural language processing and computational linguistics through practical examples and experimental results presented by Noah Smith from the University of Washington.