This book describes a study on the question of what sort of innate knowledge it is that enables children to acquire a first language. The author, using a computational approach, builds a model, named BUD (Bring Up a Daughter), on the basis of the data linguists and psychologists have collected.BUD is based on the empirists, view of first language acquisition (as opposed to that of the nativists'), that children make a number of rules in acquiring a first language and that over generalizations can be found in the acquisition of every aspect of a language. Thus, BUD has no built-in procedure by which it computes the structures of a language. A detailed description of the BUD model and its workings answers the question on which the study is based.
Human language acquisition has been studied for centuries, but using computational modeling for such studies is a relatively recent trend. However, computational approaches to language learning have become increasingly popular, mainly due to advances in developing machine learning techniques, and the availability of vast collections of experimental data on child language learning and child-adult interaction. Many of the existing computational models attempt to study the complex task of learning a language under cognitive plausibility criteria (such as memory and processing limitations that humans face), and to explain the developmental stages observed in children. By simulating the process of child language learning, computational models can show us which linguistic representations are learnable from the input that children have access to, and which mechanisms yield the same patterns of behaviour that children exhibit during this process. In doing so, computational modeling provides insight into the plausible mechanisms involved in human language acquisition, and inspires the development of better language models and techniques. This book provides an overview of the main research questions in the field of human language acquisition. It reviews the most commonly used computational frameworks, methodologies and resources for modeling child language learning, and the evaluation techniques used for assessing these computational models. The book is aimed at cognitive scientists who want to become familiar with the available computational methods for investigating problems related to human language acquisition, as well as computational linguists who are interested in applying their skills to the study of child language acquisition. Different aspects of language learning are discussed in separate chapters, including the acquisition of the individual words, the general regularities which govern word and sentence form, and the associations between form and meaning. For each of these aspects, the challenges of the task are discussed and the relevant empirical findings on children are summarized. Furthermore, the existing computational models that attempt to simulate the task under study are reviewed, and a number of case studies are presented. Table of Contents: Overview / Computational Models of Language Learning / Learning Words / Putting Words Together / Form--Meaning Associations / Final Thoughts
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.
This book describes a study on the question of what sort of innate knowledge it is that enables children to acquire a first language. The author, using a computational approach, builds a model, named BUD (Bring Up a Daughter), on the basis of the data linguists and psychologists have collected.BUD is based on the empirists, view of first language acquisition (as opposed to that of the nativists'), that children make a number of rules in acquiring a first language and that over generalizations can be found in the acquisition of every aspect of a language. Thus, BUD has no built-in procedure by which it computes the structures of a language. A detailed description of the BUD model and its workings answers the question on which the study is based.
The past fifteen years have seen great changes in the field of language acquisition. New experimental methods have yielded insights into the linguistic knowledge of ever younger children, and interest has grown in the phonological, syntactic, and semantic aspects of the lexicon. Computational investigations of language acquisition have also changed, reflecting, among other things, the profound shift in the field of natural language processing from hand-crafted grammars to grammars that are learned automatically from samples of naturally occurring language.Each of the four research papers in this book takes a novel formal approach to a particular problem in language acquisition. In the first paper, J. M. Siskind looks at developmentally inspired models of word learning. In the second, M. R. Brent and T. A. Cartwright look at how children could discover the sounds of words, given that word boundaries are not marked by any acoustic analog of the spaces between written words. In the third, P. Resnik measures the association between verbs and the semantic categories of their arguments that children likely use as clues to verb meanings. Finally, P. Niyogi and R. C. Berwick address the setting of syntactic parameters such as headedness--for example, whether the direct object comes before or after the verb.
In doing so, computational modeling provides insight into the plausible mechanisms involved in human language acquisition, and inspires the development of better language models and techniques. This book provides an overview of the main research quesetions in the field of human language acquisition. It reviews the most commonly used computational frameworks, methodologies and resources for modeling child language learning, and the evaluation techniques used for assessing these computational models. The book is aimed at cognitive scientists who want to become familiar with the available computational methods for investigating problems related to human language acquisition, as well as computational linguists who are interested in applying their skills to the study of child language acquisition.
Interest in the computational modelling of natural language acquisition has grown in both the fields of Computer Science and Psychology, yet for a variety of reasons, such modelling remains in its infancy. Several of the more recent models of language acquisition are reviewed and an indication of where the scope of such models could be broadened is given. A model incorporating several sub-tasks of language acquisition including grammar, concept and some vocabulary acquisition is then presented. Several experiments are described, which serve to illustrate the effectiveness of the current model as well as its individual components. Finally, a number of the model's shortcomings are documented and possible resolutions to these difficulties as well as an indication of where further works remains to be done, is given.
This book presents recent advances by leading researchers in computational modelling of language acquisition. Sophisticated theoretical models can now be tested using simulation techniques and large corpora of linguistic data. Renewed interest in learning neural networks and the ability to test new solutions to fundamental problems has fuelled debates in an already very active field. The twenty-four authors in this collection of new work have been drawn from departments of linguistics, cognitive science, psychology, and computer sciene. The book as a whole shows what light may be thrown on fundamental problems when powerful computational techniques are combined with real data. A central question addressed in the book concerns the extent to which linguistic structure is readily available in the environment. The authors consider the evidence in relation to word boundaries and phonotactic structure, stress patterns, text-to-speech rules, and the mapping of lexical semantics, one author arguing that a child's own output may serve as a key source of linguistic input. Linguistic structure environment relations are central to the debate on the degree to which language learning is inductive or deductive; this issue is considered here in studies of the acquisition of pluralization and inflectional morphology. The book examines the power and utility of different modelling formalisms for different problems and approaches: how far, for example, can connectionist models beused as models for language acquisition? To what degree can lexical items and categories be used in the construction of neural network models or Markov chains be deployed to investigate the characteristics of a general language learning algorithm (Triggering Learning Algorithm)? This book will appeal to linguists, psychologists, and cognitive scientists working in language acquisition. It will also interest those involved in computational modelling in linguistics and behavioural science.
