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This landmark work in computational linguistics is of great importance boththeoretically and practically because it shows that much of English grammar can be learned by asimple program.The Acquisition of Syntactic Knowledge investigates the central questions of humanand machine cognition: How do people learn language? How can we get a machine to learn language? Itfirst presents an explicit computational model of language acquisition which can actually learnrules of English syntax given a sequence of grammatical, but otherwise unprepared, sentences.Itshows that natural languages are designed to be easily learned and easily processed-an excitingbreakthrough from the point of view of artificial intelligence and the design of expert systemsbecause it shows how extensive knowledge might be acquired automatically, without outsideintervention. Computationally, the book demonstrates how constraints that may be reasonably assumedto aid sentence processing also aid language acquisition.Chapters in the book's second part applycomputational methods to the general problem of developmental growth, particularly the thornyproblem of the interaction between innate genetic endowment and environmental input, with the intentof uncovering the constraints on the acquisition of syntactic knowledge.A number of "mini-theories"of learning are incorporated in this study of syntax with results that should appeal to a wide rangeof scholarly interests. These include how lexical categories, phonological rule systems, and phrasestructure rules are learned; the role of semantic-syntactic interaction in language acquisition; howa "parameter setting" model may be formalized as a learning procedure; how multiple constraints(from syntax, thematic knowledge, or phrase structure) interact to aid acquisition; howtransformational-type rules may be learned; and, the role of lexical ambiguity in languageacquisition.Robert Berwick is an assistant professor in the Department of Electrical Engineering andComputer Science at MIT. The Acquisition of Syntactic Knowledge is sixteenth in the ArtificialIntelligence Series, edited by Patrick Winston and Michael Brady.… (meer)
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This work and it's succeeding volumes reprint stories drawn from all three volumes of the editor's The Reel West series, however, they do not match the contents of the corresponding volumes. They should not be combined.
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This landmark work in computational linguistics is of great importance boththeoretically and practically because it shows that much of English grammar can be learned by asimple program.The Acquisition of Syntactic Knowledge investigates the central questions of humanand machine cognition: How do people learn language? How can we get a machine to learn language? Itfirst presents an explicit computational model of language acquisition which can actually learnrules of English syntax given a sequence of grammatical, but otherwise unprepared, sentences.Itshows that natural languages are designed to be easily learned and easily processed-an excitingbreakthrough from the point of view of artificial intelligence and the design of expert systemsbecause it shows how extensive knowledge might be acquired automatically, without outsideintervention. Computationally, the book demonstrates how constraints that may be reasonably assumedto aid sentence processing also aid language acquisition.Chapters in the book's second part applycomputational methods to the general problem of developmental growth, particularly the thornyproblem of the interaction between innate genetic endowment and environmental input, with the intentof uncovering the constraints on the acquisition of syntactic knowledge.A number of "mini-theories"of learning are incorporated in this study of syntax with results that should appeal to a wide rangeof scholarly interests. These include how lexical categories, phonological rule systems, and phrasestructure rules are learned; the role of semantic-syntactic interaction in language acquisition; howa "parameter setting" model may be formalized as a learning procedure; how multiple constraints(from syntax, thematic knowledge, or phrase structure) interact to aid acquisition; howtransformational-type rules may be learned; and, the role of lexical ambiguity in languageacquisition.Robert Berwick is an assistant professor in the Department of Electrical Engineering andComputer Science at MIT. The Acquisition of Syntactic Knowledge is sixteenth in the ArtificialIntelligence Series, edited by Patrick Winston and Michael Brady.