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 be used 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.‎