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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.
Bridges the gap between natural language processing and cognitive sciences
Features contributions from experts in different disciplines to provide a broad, interdisciplinary approach
Develops computational models that have been tested and evaluated on real data
Table of Contents
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.