Questions related to language acquisition have been of interest for many centuries, as children seem to acquire a sophisticated capacity for processing language with apparent ease, in the face of ambiguity, noise and uncertainty. However, with recent advances in technology and cognitive-related research it is now possible to conduct large-scale computational investigations of these issues

The book discusses some of the latest theoretical and practical developments in the areas involved, including computational models for language tasks, tools and resources that help to approximate the linguistic environment available to children during acquisition, and discussions of challenging aspects of language that children have to master.

This is a much-needed collection that provides a cross-section of recent multidisciplinary research on the computational modeling of language acquisition. It is targeted at anyone interested in the relevance of computational techniques for understanding language acquisition. Readers of this book will be introduced to some of the latest approaches to these tasks including:

* Models of acquisition of various types of linguistic information (from words to syntax and semantics) and their relevance to research on human language acquisition

* Analysis of linguistic and contextual factors that influence acquisition

* Resources and tools for investigating these tasks

Each chapter is presented in a self-contained manner, providing a detailed description of the relevant aspects related to research on language acquisition, and includes illustrations and tables to complement these in-depth discussions. Though there are no formal prerequisites, some familiarity with the basic concepts of human and computational language acquisition is beneficial.

Questions related to language acquisition have been of interest for many centuries, as children seem to acquire a sophisticated capacity for processing language with apparent ease, in the face of ambiguity, noise and uncertainty. However, with recent advances in technology and cognitive-related research it is now possible to conduct large-scale computational investigations of these issues The book discusses some of the latest theoretical and practical developments in the areas involved, including computational models for language tasks, tools and resources that help to approximate the linguistic environment available to children during acquisition, and discussions of challenging aspects of language that children have to master. This is a much-needed collection that provides a cross-section of recent multidisciplinary research on the computational modeling of language acquisition. It is targeted at anyone interested in the relevance of computational techniques for understanding language acquisition. Readers of this book will be introduced to some of the latest approaches to these tasks including: * Models of acquisition of various types of linguistic information (from words to syntax and semantics) and their relevance to research on human language acquisition * Analysis of linguistic and contextual factors that influence acquisition * Resources and tools for investigating these tasks Each chapter is presented in a self-contained manner, providing a detailed description of the relevant aspects related to research on language acquisition, and includes illustrations and tables to complement these in-depth discussions. Though there are no formal prerequisites, some familiarity with the basic concepts of human and computational language acquisition is beneficial.

Information extraction (IE) and text summarization (TS) are powerful technologies for finding relevant pieces of information in text and presenting them to the user in condensed form. The ongoing information explosion makes IE and TS critical for successful functioning within the information society. These technologies face particular challenges due to the inherent multi-source nature of the information explosion. The technologies must now handle not isolated texts or individual narratives, but rather large-scale repositories and streams---in general, in multiple languages---containing a multiplicity of perspectives, opinions, or commentaries on particular topics, entities or events. There is thus a need to adapt existing techniques and develop new ones to deal with these challenges. This volume contains a selection of papers that present a variety of methodologies for content identification and extraction, as well as for content fusion and regeneration. The chapters cover various aspects of the challenges, depending on the nature of the information sought---names vs. events,--- and the nature of the sources---news streams vs. image captions vs. scientific research papers, etc. This volume aims to offer a broad and representative sample of studies from this very active research field.

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.

A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry. The dream of a universal translation device goes back many decades, long before Douglas Adams's fictional Babel fish provided this service in The Hitchhiker's Guide to the Galaxy. Since the advent of computers, research has focused on the design of digital machine translation tools-computer programs capable of automatically translating a text from a source language to a target language. This has become one of the most fundamental tasks of artificial intelligence. This volume in the MIT Press Essential Knowledge series offers a concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and market potential. The main approaches are presented from a largely historical perspective and in an intuitive manner, allowing the reader to understand the main principles without knowing the mathematical details. The book begins by discussing problems that must be solved during the development of a machine translation system and offering a brief overview of the evolution of the field. It then takes up the history of machine translation in more detail, describing its pre-digital beginnings, rule-based approaches, the 1966 ALPAC (Automatic Language Processing Advisory Committee) report and its consequences, the advent of parallel corpora, the example-based paradigm, the statistical paradigm, the segment-based approach, the introduction of more linguistic knowledge into the systems, and the latest approaches based on deep learning. Finally, it considers evaluation challenges and the commercial status of the field, including activities by such major players as Google and Systran.