TransEasy: A Chinese-English Machine Translation System Based on Hybrid Approach

1998 ◽  
pp. 514-517 ◽  
Author(s):  
Qun Liu ◽  
Shiwen Yu
Keyword(s):  
Machine Translation ◽  
Hybrid Approach ◽  
Translation System ◽  
Machine Translation System

Machine Translation Based on Translation Rules for Processing Natural Language

2014 ◽  
Vol 926-930 ◽  
pp. 2682-2685
Author(s):  
Lan Dong Li ◽  
Wen Ying Xing ◽  
Xue Long Zhang
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Hybrid Approach ◽  
Implementation Strategies ◽  
Translation System ◽  
Translation Speed ◽  
Machine Translation System ◽  
Design Ideas ◽  
Speed And Accuracy ◽  
Base Design

This paper presents a hybrid approach, which integrates an example-pattern-based method and a rule-based method, to the design and implementation of an English-Chinese machine translation system. It focuses discussion on language model , knowledge base, design ideas and implementation strategies. Our system has been tested based on requirement details listed in the Outlines for Automatic Evaluation of Machine Translation constituted by National Hi-Tech Project 863, and compared with the Huajian system. Experiment results indicate that our system has high translation speed and accuracy.

Research on the Machine Translation Based on the Hybrid Approach

2014 ◽  
Vol 687-691 ◽  
pp. 1754-1757
Author(s):  
Shu Tao Zhou
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Hybrid Approach ◽  
Implementation Strategies ◽  
Translation System ◽  
Translation Speed ◽  
Machine Translation System ◽  
Applications Of Machine Learning ◽  
Design Ideas ◽  
Base Design

This paper firstly introduces the recent research on machine translation and describes the hybrid strategies on machine translation in detail, and discusses the applications of machine learning for machine translation. The hybrid approach is a method for translation which integrates an example-pattern-based method and a rule-based method, to the design and implementation of an English-Chinese machine translation system. It focuses discussion on language model, knowledge base, design ideas and implementation strategies. Our system has been tested based on requirement details listed in the Outlines for Automatic Evaluation of Machine Translation constituted by National Hi-Tech Project, and compared with the assigned system. Experiment results indicate that our system has high translation speed and accuracy.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

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