Models of language acquisition : inductive and deductive approaches

著者

書誌事項

Models of language acquisition : inductive and deductive approaches

edited by Peter Broeder and Jaap Murre

Oxford University Press, 2000

大学図書館所蔵 件 / 全62件

この図書・雑誌をさがす

注記

Includes bibliographical references and index

内容説明・目次

内容説明

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 science. 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 childs 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 modeling formalisms for different problems and approaches: how far, for example, can connectionist models be used as models for language acquisition or Simple Recurrent Networks form the basis of a model of 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, cognitive scientists working in language acquisition. It will also interest those involved in computational modelling in linguistics and behavioural science.

目次

  • Chapter 1: Introduction
  • PART I: WORDS
  • Chapter 2: Lexicalist Connectionism
  • Chapter 3: Are SRNs Sufficient for Modelling Language Acquisition?
  • Chapter 4: A Distributed, Yet Symbolic Model for Text-to-Speech Processing
  • Chapter 5: "Lazy Learning": A Comparison of Natural and Machine Learning of Word Stress
  • PART II: WORD FORMATION
  • Chapter 6: Statistical and Connectionist Modelling of the Development of Speech Segmentation
  • Chapter 7: Learning Word-to-Meaning Mappings
  • Chapter 8: Children's Overregularization and its Implication for Cognition
  • Chapter 9: The Performance of a Recurrent Network with Short Term Memory Capacity Learning the German -S Plural
  • Chapter 19: A Cross-Linguistic Comparison of Single and Dual-Route Models of Inflectional Morphology
  • PART III: WORD ORDER
  • Chapter 11: Formal Models for Learning in the Principles and Parameters Framework
  • Chapter 12: An Output-as-Input Hypothesis for Language Acquisition: Arguments, Model, Evidence

「Nielsen BookData」 より

詳細情報

ページトップへ