Titlebook: Recent Advances in Example-Based Machine Translation; Michael Carl,Andy Way Book 2003 Kluwer Academic Publishers 2003 EBMT.algorithms.case

绑架

EBMT in a Controlled Environmentnced TM system, a . (PL), which takes advantage of the huge, underused resources available in existing translation aids. We claim that PL and EBMT systems can provide valuable translation solutions for restricted domains, especially where controlled language restrictions are imposed. When integrated

Slit-Lamp

Formalizing Translation Memory the MSSM algorithm based on dynamic programming techniques are all introduced in order to formalize Translation Memories (TM). We show how this approach leads to a real gain in recall and precision, and allows the extension of TM towards rudimentary, yet useful Example-Based Machine Translation (EB

DENT

An Example-Based Machine Translation System Using DP-Matching Between Word Sequencesng out DP-matching of the input sentence and source sentences in an example database while measuring the semantic distances of the words. Second, the approach adjusts the gap between the input and the most similar example by using a bilingual dictionary. We demonstrate its high coverage and accuracy

纪念

短程旅游

EBMT of POS-Tagged Sentences by Recursive Division Via Inductive Learning The sentence is divided according to the structure of similar examples extracted during the matching process. The approach is especially intended for languages where resources and tools are pretty much unavailable. POS taggers are the only tools utilized, and the bilingual corpus the only resource

AMPLE

Learning Translation Templates from Bilingual Translation Examplespondences are learned using analogical reasoning between two translation examples. Given two translation examples, any similarities in the source language sentences must correspond to the similar parts of the target language sentences, while any differences in the source strings must correspond to t

PANG

加强防卫

夹克怕包裹

intrigue

Extracting Translation Knowledge from Parallel Corporacally probable dependency relations to acquire word and phrasal correspondences. We obtained 90% precision using an English-Japanese parallel corpus of 9268 sentences in the business domain. The result showed that statistically probable dependency relations are effective in translation knowledge acq