Probabilistic Approaches to Linguistic Theory

In this collection of papers, leading researchers in computational and formal linguistics present new approaches to the central questions of linguistic theory, explore the role of probabilistic and machine learning methods in the development of linguistic theories, and reveal new applications of recent learning and modeling methods in AI. Through a highly interdisciplinary framework, this book tackles central questions in syntax, semantics, pragmatics, sentence processing, and dialogue, offering insights that go well beyond traditional linguistic orthodoxy. New and non-traditional approaches to longstanding issues allow the researchers to challenge and set aside entrenched theories, in favor of experimentally and computationally robust new models. This book is a must-have for researchers and graduate students in linguistics, computer science (AI and NLP), psychology, and cognitive science, and a useful reference work for industrial research labs and AI systems development.