000			04177nam a2200529 i 4500
001			6267371
003			IEEE
005			20220712204644.0
006			m o d
007			cr |n|||||||||
008			151223s1985 maua ob 001 eng d
020			_a9780262268394 _qebook
020			_z0585368910 _qelectronic
020			_z9780585368917 _qelectronic
020			_z0262268396 _qelectronic
020			_z9780262022262 _qprint
035			_a(CaBNVSL)mat06267371
035			_a(IDAMS)0b000064818b4375
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ335 _b.B48 1985eb
100	1		_aBerwick, Robert C., _eauthor. _921632
245	1	4	_aThe acquisition of syntactic knowledge / _cRobert C. Berwick.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1985.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1985]
300			_a1 PDF (xii, 368 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aThe MIT Press series in artificial intelligence
504			_aIncludes bibliographical references (p. [343]-353) and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aThis landmark work in computational linguistics is of great importance both theoretically and practically because it shows that much of English grammar can be learned by a simple program.The Acquisition of Syntactic Knowledge investigates the central questions of human and machine cognition: How do people learn language? How can we get a machine to learn language? It first presents an explicit computational model of language acquisition which can actually learn rules of English syntax given a sequence of grammatical, but otherwise unprepared, sentences.It shows that natural languages are designed to be easily learned and easily processed-an exciting breakthrough from the point of view of artificial intelligence and the design of expert systems because it shows how extensive knowledge might be acquired automatically, without outside intervention. Computationally, the book demonstrates how constraints that may be reasonably assumed to aid sentence processing also aid language acquisition.Chapters in the book's second part apply computational methods to the general problem of developmental growth, particularly the thorny problem of the interaction between innate genetic endowment and environmental input, with the intent of 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 range of scholarly interests. These include how lexical categories, phonological rule systems, and phrase structure rules are learned; the role of semantic-syntactic interaction in language acquisition; how a "parameter setting" model may be formalized as a learning procedure; how multiple constraints (from syntax, thematic knowledge, or phrase structure) interact to aid acquisition; how transformational-type rules may be learned; and, the role of lexical ambiguity in language acquisition.Robert Berwick is an assistant professor in the Department of Electrical Engineering and Computer Science at MIT. The Acquisition of Syntactic Knowledge is sixteenth in the Artificial Intelligence Series, edited by Patrick Winston and Michael Brady.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aLearning _xMathematical models. _922082
650		0	_aLanguage acquisition. _922403
650		0	_aComputational linguistics. _96146
650		0	_aArtificial intelligence. _93407
655		0	_aElectronic books. _93294
710	2		_aIEEE Xplore (Online Service), _edistributor. _922404
710	2		_aMIT Press, _epublisher. _922405
776	0	8	_iPrint version _z9780262022262
830		0	_aMIT Press series in artificial intelligence. _921636
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267371
942			_cEBK
999			_c73026 _d73026