NATIONAL BIBLIOGRAPHY NUMBER
Country Code
IR
Number
T16
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
COUNTRY OF PUBLICATION OR PRODUCTlON
Country of publication
IR
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Learning English Pronominals from Input And General-Purpose Learning Mechanis (Simulated by Artificial Neural Networks)
General Material Designation
[Thesis]
First Statement of Responsibility
/محمدرضایوسفی حلوایی
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
Tabriz: tabriz university
Date of Publication, Distribution, etc.
, 1388
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
86 P.
GENERAL NOTES
Text of Note
مکانیزم های آموزش
Text of Note
داده
Text of Note
شبکه های عصبی مصنوعی
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Print
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Bibiography
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
M.A.
Discipline of degree
, English Language Teaching
Date of degree
1388/07/05
SUMMARY OR ABSTRACT
Text of Note
For the last half of twentieth century Generative Linguists following Chomsky have argued that grammar is innate, rule-governed and exists in brain as domain-specific module. One of the areas of language that attracted their attention the most is the complex system of referential elements in any language. This complexity is often discussed and construed as an indication of the innate predispositions for language learning. This study is done on the learnability of some of these pronouns by input and general-purpose learning mechanisms.the possibility of learning English pronominal elements in connectionist networks was investigated. The selected pronouns were he, she, him, her, himself and herself. These were tested in two different connectionist networks. First a feed forward back propagation network and second an Elman recurrent network. What the obtained results wholly indicate is the possibility of learning English pronouns by artificial neural network. But there were some inconsistencies between the results obtained from these two networks. First in almost all the simulations, the results obtained from the Elman network were closer to desired values. Then there were some cases where both a pronoun and its referent, the proper name, were activated. Also the networks' power for generalization to novel names was not as good as the encountered names. The reason for these behaviors of the networks is discussed in the related chapter. The findings of this study contribute to the emergentist and associationist accounts of language acquisition. It has the theoretical implication that the role of innate language-specific predispositions for language acquisition is not as much important as it is asserted in Generativism and there is the possibility of acquiring pronominal elements in English by data and general-purpose learning mechanisms..
TRANSLATED TITLE SUPPLIED BY CATALOGUER
Translated Title
یادگیری ضمائر انگلیسی با داده و مکانیزم های عمومی آموزش (شبیه سازی با شبکه های عصبی مصنوعی)
TOPICAL NAME USED AS SUBJECT
Artificial Neural Networks
Connectionism
Input
Learning Mechanisms
PERSONAL NAME - PRIMARY RESPONSIBILITY
Yousefi Halvaei, Mohammad Reza
PERSONAL NAME - SECONDARY RESPONSIBILITY
Ansarin, Ali Akbar, Supervisor
Feizi Derakhshi, Mohammad, Co-supervisor
ORIGINATING SOURCE
Country
ایران
Date of Transaction
20211010
LOCATION AND CALL NUMBER
Call Number
پایان نامهPE,1127,.Y6L3,1388
ELECTRONIC LOCATION AND ACCESS
Host name
یادگیری ضمائر انگلیسی با داده و مکانیزم های عمومی آموزش ) شبیه سازی با شبکه های عصبی مصنوعی(
Access number
فوق سری
Compression information
عادی
Electronic name
E,yosefi.doc
Bits per second
1037312
Contact for access assistance
کاربر
Electronic Format Type
متن
File size
0
Record control number
T 16
Public note
سیاه و سفید
DATA NOT CONVERTED FROM SOURCE FORMAT
Tag of the Source Format Field
Location: Central library
old catalog
p
TL
276903
1
a
Y
