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Self-Organizing Example-Based Machine Translation, A Prototype.

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Title: Self-Organizing Example-Based Machine Translation, A Prototype.
Language: English
Authors: Juola, Patrick
Peer Reviewed: N
Page Count: 8
Publication Date: 1995
Document Type: Reports - Descriptive
Descriptors: Algorithms, Computational Linguistics, English, Evaluation Methods, French, Grammar, Machine Translation, Uncommonly Taught Languages, Urdu
Geographic Terms: U.S.; Colorado
Abstract: This paper describes an attempt to combine the advantages of both example-based translation and stochastic translation methods in an attempt to develop a method for inferring symbolic transfer functions from a bilingual corpus. By formalizing the translation process and applying standard optimization techniques, a system can be developed that will identify grammatical categories and produce coherent transfer functions between languages. The validity of this approach is demonstrated in a prototype system that can learn transfer functions between English, French, and Urdu. Contains 13 references. (Author)
Journal Code: RIEAUG1995
Entry Date: 1995
Accession Number: ED380987
Database: ERIC
FullText Text:
  Availability: 0
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  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED380987
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PubType: Report
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  Data: Self-Organizing Example-Based Machine Translation, A Prototype.
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  Data: 8
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  Data: 1995
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  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22English%22">English</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22French%22">French</searchLink><br /><searchLink fieldCode="DE" term="%22Grammar%22">Grammar</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Translation%22">Machine Translation</searchLink><br /><searchLink fieldCode="DE" term="%22Uncommonly+Taught+Languages%22">Uncommonly Taught Languages</searchLink><br /><searchLink fieldCode="DE" term="%22Urdu%22">Urdu</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22U%2ES%2E%3B+Colorado%22">U.S.; Colorado</searchLink>
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  Data: This paper describes an attempt to combine the advantages of both example-based translation and stochastic translation methods in an attempt to develop a method for inferring symbolic transfer functions from a bilingual corpus. By formalizing the translation process and applying standard optimization techniques, a system can be developed that will identify grammatical categories and produce coherent transfer functions between languages. The validity of this approach is demonstrated in a prototype system that can learn transfer functions between English, French, and Urdu. Contains 13 references. (Author)
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      – Text: English
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        PageCount: 8
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: English
        Type: general
      – SubjectFull: Evaluation Methods
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      – SubjectFull: French
        Type: general
      – SubjectFull: Grammar
        Type: general
      – SubjectFull: Machine Translation
        Type: general
      – SubjectFull: Uncommonly Taught Languages
        Type: general
      – SubjectFull: Urdu
        Type: general
      – SubjectFull: U.S.; Colorado
        Type: general
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      – TitleFull: Self-Organizing Example-Based Machine Translation, A Prototype.
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              M: 01
              Type: published
              Y: 1995
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