Language, Cognition, and Computational Models

This book uses recent computational models to explore issues related to language and cognition.

Thierry Poibeau (Edited by), Aline Villavicencio (Edited by)

9781107162228, Cambridge University Press

Hardback, published 25 January 2018

336 pages
23.5 x 15.8 x 2.5 cm, 0.6 kg

'This volume brings a uniquely interdisciplinary approach to its subjects … All of the essays present new and emerging research in neuroscience and language. While the materials covered are advanced for most undergraduates, this volume would make a valuable resource for researchers in the field and a helpful guide to graduate students on subjects for further research. An excellent addition to collections where natural language processing and cognitive science are studied. Summing Up: Recommended.' R. Bharath, Choice

How do infants learn a language? Why and how do languages evolve? How do we understand a sentence? This book explores these questions using recent computational models that shed new light on issues related to language and cognition. The chapters in this collection propose original analyses of specific problems and develop computational models that have been tested and evaluated on real data. Featuring contributions from a diverse group of experts, this interdisciplinary book bridges the gap between natural language processing and cognitive sciences. It is divided into three sections, focusing respectively on models of neural and cognitive processing, data driven methods, and social issues in language evolution. This book will be useful to any researcher and advanced student interested in the analysis of the links between the brain and the language faculty.

Part I. About This Book: 1. Introduction T. Poibeau and A. Villavicencio
Part II. Models of Neural and Cognitive Processing: 2. Light-and-deep parsing P. Blache
3. Decoding language from brain B. Murphy, A. Fyshe and L. Wehbe
4. Graph theory applied to speech N. B. Mota, M. Copelli and S. Ribeiro
Part III. Data-Driven Models: 5. Putting linguistics back into computational linguistics M. Kay
6. A distributional model of verb-specific semantic roles inferences G. E. Lebani and A. Lenci
7. Native language identification on EFCAMDAT X. Jiang, Y. Huang, Y. Guo, J. Geertzen, T. Alexopoulou, L. Sun, A. Korhonen
8. Evaluating language acquisition models L. Pearl and L. Phillips
Part IV. Social and Language Evolution: 9. Social evolution of public languages A. Reboul
10. Genetic biases in language R. Janssen and D. Dediu
11. Transparency versus processing efficiency R. van Trijp.

Subject Areas: Natural language & machine translation [UYQL], Artificial intelligence [UYQ], Cognition & cognitive psychology [JMR], Computational linguistics [CFX]