Machine translation:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2015]
|
| Schriftenreihe: | A Chapman & Hall book
|
| Links: | https://doi.org/10.1201/b18004 https://doi.org/10.1201/b18004 https://doi.org/10.1201/b18004 https://www.taylorfrancis.com/books/9781439897195 |
| Abstract: | Three paradigms have dominated machine translation (MT)-rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT-analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination-data supplies translation parts that rules recombine to produce translation.Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text:Introduces MT against the backdrop of language divergence and the Vauquois trianglePresents expectation maximization (EM)-based word alignment as a turning point in the history of MTDiscusses the most important element of SMT-bilingual word alignment from pairs of parallel translationsExplores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentenceCovers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environmentProvides complete walk-throughs of the working of interlingua-based and transfer-based RBMTAnalyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automaticallyIncludes numerous examples that illustrate universal translation phenomena through the usage of specific languagesMachine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (260 Seiten) Diagramme |
| ISBN: | 9780429086298 |
| DOI: | 10.1201/b18004 |
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| 245 | 1 | 0 | |a Machine translation |c Pushpak Bhattacharyya, Indian Institute of Technology Bombay, Mumbai, India |
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| 490 | 0 | |a A Chapman & Hall book | |
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| 520 | 3 | |a Three paradigms have dominated machine translation (MT)-rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT-analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination-data supplies translation parts that rules recombine to produce translation.Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text:Introduces MT against the backdrop of language divergence and the Vauquois trianglePresents expectation maximization (EM)-based word alignment as a turning point in the history of MTDiscusses the most important element of SMT-bilingual word alignment from pairs of parallel translationsExplores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentenceCovers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environmentProvides complete walk-throughs of the working of interlingua-based and transfer-based RBMTAnalyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automaticallyIncludes numerous examples that illustrate universal translation phenomena through the usage of specific languagesMachine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers | |
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Datensatz im Suchindex
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| author | Bhattacharyya, Pushpak 1962- |
| author_GND | (DE-588)1167875788 |
| author_facet | Bhattacharyya, Pushpak 1962- |
| author_role | aut |
| author_sort | Bhattacharyya, Pushpak 1962- |
| author_variant | p b pb |
| building | Verbundindex |
| bvnumber | BV047105176 |
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| collection | ZDB-7-TFC |
| ctrlnum | (OCoLC)1232509900 (DE-599)BVBBV047105176 |
| dewey-full | 418/.020285 |
| dewey-hundreds | 400 - Language |
| dewey-ones | 418 - Applied linguistics |
| dewey-raw | 418/.020285 |
| dewey-search | 418/.020285 |
| dewey-sort | 3418 520285 |
| dewey-tens | 410 - Linguistics |
| discipline | Sprachwissenschaft Literaturwissenschaft |
| doi_str_mv | 10.1201/b18004 |
| format | Electronic eBook |
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| id | DE-604.BV047105176 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:09:49Z |
| institution | BVB |
| isbn | 9780429086298 |
| language | English |
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| oclc_num | 1232509900 |
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| physical | 1 Online-Ressource (260 Seiten) Diagramme |
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| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | CRC Press, Taylor & Francis Group |
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| series2 | A Chapman & Hall book |
| spelling | Bhattacharyya, Pushpak 1962- Verfasser (DE-588)1167875788 aut Machine translation Pushpak Bhattacharyya, Indian Institute of Technology Bombay, Mumbai, India Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2015] 1 Online-Ressource (260 Seiten) Diagramme txt rdacontent c rdamedia cr rdacarrier A Chapman & Hall book Includes bibliographical references and index Three paradigms have dominated machine translation (MT)-rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT-analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination-data supplies translation parts that rules recombine to produce translation.Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text:Introduces MT against the backdrop of language divergence and the Vauquois trianglePresents expectation maximization (EM)-based word alignment as a turning point in the history of MTDiscusses the most important element of SMT-bilingual word alignment from pairs of parallel translationsExplores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentenceCovers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environmentProvides complete walk-throughs of the working of interlingua-based and transfer-based RBMTAnalyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automaticallyIncludes numerous examples that illustrate universal translation phenomena through the usage of specific languagesMachine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers Erscheint auch als Druck-Ausgabe 9781439897188 Erscheint auch als Druck-Ausgabe 978-1-4398-9719-5 https://doi.org/10.1201/b18004 Resolving-System Volltext https://www.taylorfrancis.com/books/9781439897195 Verlag Volltext |
| spellingShingle | Bhattacharyya, Pushpak 1962- Machine translation |
| title | Machine translation |
| title_auth | Machine translation |
| title_exact_search | Machine translation |
| title_full | Machine translation Pushpak Bhattacharyya, Indian Institute of Technology Bombay, Mumbai, India |
| title_fullStr | Machine translation Pushpak Bhattacharyya, Indian Institute of Technology Bombay, Mumbai, India |
| title_full_unstemmed | Machine translation Pushpak Bhattacharyya, Indian Institute of Technology Bombay, Mumbai, India |
| title_short | Machine translation |
| title_sort | machine translation |
| url | https://doi.org/10.1201/b18004 https://www.taylorfrancis.com/books/9781439897195 |
| work_keys_str_mv | AT bhattacharyyapushpak machinetranslation |