Recent Advances in Example-Based Machine Translation

ISBN-13: 9781402014000 / Angielski / Twarda / 2003 / 482 str.

Michael Ed Carl; M. Carl; Andy Way

Opis

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.

Recenzje

Michael Carl and Andy Way have done the field of MT and that of natural language processing in general a big favor by producing this collection. This is the first (fat) book-sized collection of current research in this fascinating area of MT, and it contains a number of chapters that will bring anyone interested quickly up to speed in this research area. ...

I would recommend this book to everyone active or interested in MT, and especially the papers of the foundational part I to computational linguistics researchers in general.

Reviewed by:
Walter Daelemans, University of Antwerp
in: Computational Linguistics, Volume 30, Number 4

Spis treści

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.