Research on language analysis of English translation system based on fuzzy algorithm

2020 ◽  
pp. 1-9
Author(s):  
Zhiqian Yuan ◽  
Chaoyang Jin ◽  
Zhaojun Chen
Keyword(s):  
Image Recognition ◽  
English Translation ◽  
Rapid Development ◽  
Language Translation ◽  
Translation System ◽  
Fuzzy Algorithm ◽  
Language Analysis ◽  
Gaussian Blur ◽  
Language Characteristics ◽  
Translation Accuracy

In recent years, with the rapid development of computer technology, the need for barrier-free communication between people of all countries have become more and more urgent. Therefore, it is extremely important to establish a high translation accuracy and high-quality English translation system. At present, Although the various English translation systems on the market have solved the communication problems between different languages to a certain extent, there are a series of problems such as language translation ambiguity and inaccurate use of words in translation methods, in order to improve English The translation accuracy of the translation system can improve the quality of the English translation system. This paper proposes a language analysis study of the English translation system based on fuzzy algorithms. The research of this paper firmly grasps the analysis and understanding of the language, analyzes it from the corpus, vocabulary, syntax, and translation characteristics, and fully understands its language characteristics, so as to eliminate the semantic understanding ambiguity in the translation process to a certain extent. Thereby improving the accuracy of the translation. The English translation system designed in this paper includes an image input module and an image recognition module, so the Gaussian blur algorithm is used for processing. The Gaussian blur algorithm can retain edge information in the edge area where the pixel value of the image changes sharply, and can effectively remove noise and enhance the image effect. Therefore, this article uses fuzzy algorithm-based English translation system language analysis research, first analyze the English language characteristics, and then use Gaussian fuzzy algorithm to denoise the image in the translation system, and then display the image recognition results.

scholarly journals Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guangjun Dong ◽  
Youchao Yang ◽  
Qiankun Zhang
Keyword(s):  
Feature Extraction ◽  
English Translation ◽  
Selection Process ◽  
Translation System ◽  
Chinese Translation ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Optimal Feature Selection ◽  
Translation Accuracy ◽  
Optimal Feature

In the process of English translation, traditional interactive English translation system is not obvious in English semantic context. The optimal feature selection process does not achieve the optimal translation solution, and the translation accuracy is low. Based on this, this paper designs an interactive English Chinese translation system based on a feature extraction algorithm. By introducing the feature extraction algorithm, the optimal translation solution is selected, and the semantic mapping model is constructed to translate the best translation into English Chinese translation. The real experiment results show that the interactive English Chinese translation system based on feature extraction algorithm can get the best solution.

scholarly journals Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuping Ren
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Structure Analysis ◽  
English Translation ◽  
Language Translation ◽  
Translation Method ◽  
Translation System ◽  
Chinese Translation ◽  
Lexical Structure ◽  
Traditional Language

Language translation is often conducted in work and study. Traditional language translation is based on lexical structure analysis. However, natural language is not so standardized, which causes this translation method to have fundamental defects, no matter how much the algorithm is improved. The translation results and human translation will be very different. This paper mainly studies the networked artificial intelligence. The English translation system and translation methods are based on a smart knowledge base. Bringing an example of English-Chinese translation to suggest translations according to the intelligent knowledge base explains in detail the principle of intelligent knowledge-based translation and the advantage of this translation method compared with the traditional translation method based on lexical structure analysis. In the experiment of this paper, when the variance is 2/N, 30/N, 100/N, and 2N, it is the experimental data for an in-depth study. When the variance is 2/N, 30/N, and 100/N, the result is the same as that when the variance is 0.5; the result when the variance is 2N also conforms to the trend in the tables, which is close to the effect of the smoothing algorithm, which verifies the effectiveness of the system in this paper.

scholarly journals Research on Intelligent English Translation Method Based on the Improved Attention Mechanism Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Rong Wang
Keyword(s):  
Neural Network ◽  
English Translation ◽  
Attention Mechanism ◽  
Machine Learning Algorithms ◽  
Translation System ◽  
Test Results ◽  
Long Distance ◽  
Distance Information ◽  
Mechanism Model ◽  
Translation Accuracy

The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model.

A spoken language translation system

1992 ◽  
Author(s):  
Tsuyoshi Morimoto ◽  
Masami Suzuki ◽  
Toshiyuki Takezawa ◽  
Gen'ichiro Kikui ◽  
Masaaki Nagata ◽  
...  
Keyword(s):  
Language Translation ◽  
Spoken Language ◽  
Translation System ◽  
Spoken Language Translation

Research on Machine Translation Technology in English Translation Process

2014 ◽  
Vol 687-691 ◽  
pp. 1210-1213
Author(s):  
Ke Tian
Keyword(s):  
Machine Translation ◽  
English Translation ◽  
Development Process ◽  
Translation System ◽  
Translation Process ◽  
Sentence Meaning ◽  
Cultural Exchanges ◽  
Response Measures ◽  
World Economic ◽  
Translation Machine

Translation plays an important role in the world economic and cultural exchanges. Translation is divided into machine translation and human translation, which is complement each other in promoting world economic and social development process. In this paper, Collaborative Translation gets much attention, along with the growth of collaborative translation, English translation technology also towards a new milestone, the characteristics of collaborative translation process and scientific literature are briefly introduced, and collaborative translation technology English Translation applications made a brief explanation. From the perspective of the development of machine translation, comparative analysis of the characteristics of human translation machine translation strengths and weaknesses, and we make relevant response measures and selection criteria translation approach. The specific translation system is analyzed from the perspective of textual and the Collaborative Translation shortcomings, as well as interpretation of collaborative translation features, functions and its impact on the meaning and sentence meaning.

Improved neural machine translation for low-resource English–Assamese pair

2021 ◽  
pp. 1-12
Author(s):  
Sahinur Rahman Laskar ◽  
Abdullah Faiz Ur Rahman Khilji ◽  
Partha Pakray ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Data Augmentation ◽  
Language Translation ◽  
Linguistically Diverse ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Parallel Data ◽  
The World ◽  
Translation Accuracy ◽  
Vocabulary Problems

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

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