Recent Advances in Example-Based Machine Translation

2003
ISBN: 978-1-4020-1400-0
Verlag: Springer Netherlands

Buch, Englisch, Band 21, 482 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 1990 g

Reihe: Text, Speech and Language Technology

Recent Advances in Example-Based Machine Translation is of relevance to researchers and program developers in the field of Machine Translation and especially Example-Based Machine Translation, bilingual text processing and cross-linguistic information retrieval. It is also of interest to translation technologists and localisation professionals.

Recent Advances in Example-Based Machine Translation fills a void, because it is the first book to tackle the issue of EBMT in depth. It gives a state-of-the-art overview of EBMT techniques and provides a coherent structure in which all aspects of EBMT are embedded. Its contributions are written by long-standing researchers in the field of MT in general, and EBMT in particular. This book can be used in graduate-level courses in machine translation and statistical NLP.

Zielgruppe

Research

Autoren/Hrsg.

Way, Andy

Carl, M.

Fachgebiete

• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Geisteswissenschaften Sprachwissenschaft Übersetzungswissenschaft, Translatologie, Dolmetschen
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung

Inhaltsverzeichnis

I Foundations of EBMT.- 1 An Overview of EBMT.- 2 What is Example-Based Machine Translation?.- 3 Example-Based Machine Translation in a Controlled Environment.- 4 EBMT Seen as Case-based Reasoning.- II Run-time Approaches to EBMT.- 5 Formalizing Translation Memory.- 6 EBMT Using DP-Matching Between Word Sequences.- 7 A Hybrid Rule and Example-Based Method for Machine Translation.- 8 EBMT of POS-Tagged Sentences via Inductive Learning.- III Template-Driven EBMT.- 9 Learning Translation Templates from Bilingual Translation Examples.- 10 Clustered Transfer Rule Induction for Example-Based Translation.- 11 Translation Patterns, Linguistic Knowledge and Complexity in EBMT.- 12 Inducing Translation Grammars from Bracketed Alignments.- IV EBMT and Derivation Trees.- 13 Extracting Translation Knowledge from Parallel Corpora.- 14 Finding Translation Patterns from Dependency Structures.- 15 A Best-First Alignment Algorithm for Extraction of Transfer Mappings.- 16 Translating with Examples: The LFG-DOT Models of Translation.