Probabilistic Approaches to Linguistic Theory

A textbook exploring predictive modes of linguistic development and analysis.

During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science.
 

Jean-Philippe Bernardy is a researcher in the Linguistics and Theory of Science unit at the University of Gothenburg. 
 

Rasmus Blanck is a lecturer in logic and theoretical philosophy at the University of Gothenburg. 

Stergios Chatzikyriakidis is professor of computational linguistics at the University of Crete.

Shalom Lappin is a senior researcher in the Linguistics and Theory of Science unit at the University of Gothenburg.

Aleksandre Maskharashvili is a visiting professor in the Department of Linguistics at Ohio State University.