語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Translation, Brains and the Computer...
~
Scott, Bernard.
Translation, Brains and the Computer = A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Translation, Brains and the Computer/ by Bernard Scott.
其他題名:
A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /
作者:
Scott, Bernard.
面頁冊數:
XVI, 241 p. 55 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Natural language processing (Computer science). -
電子資源:
https://doi.org/10.1007/978-3-319-76629-4
ISBN:
9783319766294
Translation, Brains and the Computer = A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /
Scott, Bernard.
Translation, Brains and the Computer
A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /[electronic resource] :by Bernard Scott. - 1st ed. 2018. - XVI, 241 p. 55 illus.online resource. - Machine Translation: Technologies and Applications,22522-8021 ;. - Machine Translation: Technologies and Applications,2.
1 Introduction -- 2 Background -- Logos Model Beginnings -- Advent of Statistical MT -- Overview of Logos Model Translation Process -- Psycholinguistic and Neurolinguistic Assumptions -- On Language and Grammar -- Conclusion -- 3 – Language and Ambiguity: Psycholinguistic Perspectives -- Levels of Ambiguity -- Language Acquisition and Translation -- Psycholinguistic Bases of Language Skills -- Practical Implications for Machine Translation -- Psycholinguistics in a Machine -- Conclusion -- 4– Language and Complexity: Neurolinguistic Perspectives -- Cognitive Complexity -- A Role for Semantic Abstraction -- Connectionism and Brain Simulation -- Logos Model as a Neural Network -- Language Processing in the Brain -- MT Performance and Underlying Competence -- Conclusion -- 5 – Syntax and Semantics: Dichotomy or Integration? -- Syntax versus Semantics: Is There a Third, Semantico- Syntactic Perspective? -- Recent Views of the Cerebral Process -- Syntax and Semantics: How Do They Relate? -- Conclusion -- 6 –Logos Model: Design and Performance -- The Translation Problem -- How Do You Represent Natural Language? -- How Do You Store Linguistic Knowledge? -- How Do You Apply Stored Knowledge To The Input Stream? -- How do you Effect Target Transfer and Generation? -- How Do You Deal with Complexity Issues? -- Conclusion -- 7 – Some limits on Translation Quality -- First Example -- Second Example -- Other Translation Examples -- Balancing the Picture -- Conclusion -- 8 – Deep Learning MT and Logos Model -- Points of Similarity and Differences -- Deep Learning, Logos Model and the Brain -- On Learning -- The Hippocampus Again -- Conclusion -- Part II -- The SAL Representation Language -- SAL Nouns -- SAL Verbs -- SAL Adjectives -- SAL Adverbs.
This book is about machine translation (MT) and the classic problems associated with this language technology. It examines the causes of these problems and, for linguistic, rule-based systems, attributes the cause to language’s ambiguity and complexity and their interplay in logic-driven processes. For non-linguistic, data-driven systems, the book attributes translation shortcomings to the very lack of linguistics. It then proposes a demonstrable way to relieve these drawbacks in the shape of a working translation model (Logos Model) that has taken its inspiration from key assumptions about psycholinguistic and neurolinguistic function. The book suggests that this brain-based mechanism is effective precisely because it bridges both linguistically driven and data-driven methodologies. It shows how simulation of this cerebral mechanism has freed this one MT model from the all-important, classic problem of complexity when coping with the ambiguities of language. Logos Model accomplishes this by a data-driven process that does not sacrifice linguistic knowledge, but that, like the brain, integrates linguistics within a data-driven process. As a consequence, the book suggests that the brain-like mechanism embedded in this model has the potential to contribute to further advances in machine translation in all its technological instantiations.
ISBN: 9783319766294
Standard No.: 10.1007/978-3-319-76629-4doiSubjects--Topical Terms:
802180
Natural language processing (Computer science).
LC Class. No.: QA76.9.N38
Dewey Class. No.: 006.35
Translation, Brains and the Computer = A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /
LDR
:04607nam a22004095i 4500
001
994264
003
DE-He213
005
20200703004226.0
007
cr nn 008mamaa
008
201225s2018 gw | s |||| 0|eng d
020
$a
9783319766294
$9
978-3-319-76629-4
024
7
$a
10.1007/978-3-319-76629-4
$2
doi
035
$a
978-3-319-76629-4
050
4
$a
QA76.9.N38
072
7
$a
UYQL
$2
bicssc
072
7
$a
COM073000
$2
bisacsh
072
7
$a
UYQL
$2
thema
082
0 4
$a
006.35
$2
23
100
1
$a
Scott, Bernard.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1205891
245
1 0
$a
Translation, Brains and the Computer
$h
[electronic resource] :
$b
A Neurolinguistic Solution to Ambiguity and Complexity in Machine Translation /
$c
by Bernard Scott.
250
$a
1st ed. 2018.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2018.
300
$a
XVI, 241 p. 55 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Machine Translation: Technologies and Applications,
$x
2522-8021 ;
$v
2
505
0
$a
1 Introduction -- 2 Background -- Logos Model Beginnings -- Advent of Statistical MT -- Overview of Logos Model Translation Process -- Psycholinguistic and Neurolinguistic Assumptions -- On Language and Grammar -- Conclusion -- 3 – Language and Ambiguity: Psycholinguistic Perspectives -- Levels of Ambiguity -- Language Acquisition and Translation -- Psycholinguistic Bases of Language Skills -- Practical Implications for Machine Translation -- Psycholinguistics in a Machine -- Conclusion -- 4– Language and Complexity: Neurolinguistic Perspectives -- Cognitive Complexity -- A Role for Semantic Abstraction -- Connectionism and Brain Simulation -- Logos Model as a Neural Network -- Language Processing in the Brain -- MT Performance and Underlying Competence -- Conclusion -- 5 – Syntax and Semantics: Dichotomy or Integration? -- Syntax versus Semantics: Is There a Third, Semantico- Syntactic Perspective? -- Recent Views of the Cerebral Process -- Syntax and Semantics: How Do They Relate? -- Conclusion -- 6 –Logos Model: Design and Performance -- The Translation Problem -- How Do You Represent Natural Language? -- How Do You Store Linguistic Knowledge? -- How Do You Apply Stored Knowledge To The Input Stream? -- How do you Effect Target Transfer and Generation? -- How Do You Deal with Complexity Issues? -- Conclusion -- 7 – Some limits on Translation Quality -- First Example -- Second Example -- Other Translation Examples -- Balancing the Picture -- Conclusion -- 8 – Deep Learning MT and Logos Model -- Points of Similarity and Differences -- Deep Learning, Logos Model and the Brain -- On Learning -- The Hippocampus Again -- Conclusion -- Part II -- The SAL Representation Language -- SAL Nouns -- SAL Verbs -- SAL Adjectives -- SAL Adverbs.
520
$a
This book is about machine translation (MT) and the classic problems associated with this language technology. It examines the causes of these problems and, for linguistic, rule-based systems, attributes the cause to language’s ambiguity and complexity and their interplay in logic-driven processes. For non-linguistic, data-driven systems, the book attributes translation shortcomings to the very lack of linguistics. It then proposes a demonstrable way to relieve these drawbacks in the shape of a working translation model (Logos Model) that has taken its inspiration from key assumptions about psycholinguistic and neurolinguistic function. The book suggests that this brain-based mechanism is effective precisely because it bridges both linguistically driven and data-driven methodologies. It shows how simulation of this cerebral mechanism has freed this one MT model from the all-important, classic problem of complexity when coping with the ambiguities of language. Logos Model accomplishes this by a data-driven process that does not sacrifice linguistic knowledge, but that, like the brain, integrates linguistics within a data-driven process. As a consequence, the book suggests that the brain-like mechanism embedded in this model has the potential to contribute to further advances in machine translation in all its technological instantiations.
650
0
$a
Natural language processing (Computer science).
$3
802180
650
0
$a
Computational linguistics.
$3
555811
650
0
$a
Psycholinguistics.
$3
555290
650
1 4
$a
Natural Language Processing (NLP).
$3
1254293
650
2 4
$a
Computational Linguistics.
$3
670080
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319766287
776
0 8
$i
Printed edition:
$z
9783319766300
776
0 8
$i
Printed edition:
$z
9783030095383
830
0
$a
Machine Translation: Technologies and Applications,
$x
2522-8021 ;
$v
2
$3
1285593
856
4 0
$u
https://doi.org/10.1007/978-3-319-76629-4
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入