Functional Equivalence and Translation of English Long Sentences

Since English long possess a lot of modifiers and their syntax structures are complicated, it is difficult for the Chinese readers to understand them, not to mention translating them. The paper adopts Nida's theory of functional equivalence as the guideline in the process of translation, since it bears the merit of facilitating the communication of information. In terms of concrete methods, the long sentence should be decomposed into kernel sentences and reconstructed according to the expression of the standard Chinese language. Only by doing that, the translation version can be faithful, correct and elegant.

An Analysis of Translating Techniques of Long and Complex English Sentences

Long and complex English sentences, just as the name implies, are long sentences with complicated structures. The most typical features of this kind of sentences are the comprehensive use of different subordinate clauses and the flexible use of all kinds of prepositional phrases and participle phrases, which lead to complex sentence structures and varied constituent relationships, and bring great difficulties to English-Chinese translation practice. The paper concludes that, in the translation of long and complex English sentences, the translator should first figure out the logic relationship of the original text, and then, according to the Chinese expression customs, make a flexible use of various translation methods and techniques, and finally he can get a faithful and smooth translated text.

Translation Strategies of Long and Difficult Journalistic Sentences From the Perspective of Semantic Translation and Communicative Translation Theory

Peter Newmark’s semantic translation and communicative translation theories play a guiding role in translation practice.Translating long and difficult English sentences into Chinese has been a focus of study among translators.The paper explores how to translate long and difficult journalistic English sentences into Chinese from the perspective of semantic translation and communicative translation theories,advancing four effective translating strategies for handling long and difficult journalistic English sentences:sequential translation,splitting translation,reversing translation,and recasting translation,so as to improve the readability and faithfulness of the Chinese version.

On the Translation Strategies for English Business Contracts

With advancing of economic globalization and China's Reform and Opening up, the business relations between China and foreign countries are becoming frequent. Business contract is a binding text which is essential to bilateral economy interests.The translation of English business contract concerns both the interests of trade partners and countries, and it is also an important part of economic diplomacy. But in recent years, many inaccurate, unprofessional translation of English business contract can be seen in business circle. This phenomenon can mess up the market and lead to huge loss of economy benefits. So we can see clearly it is important for us to translate contracts precisely. To accomplish this goal, we must be guided with proper strategies. The thesis discusses the features of English business contracts and then tries to find the strategies for translating English Business Contracts.

English Text Named Entity Recognition Method by Fusing Local and Global Features

Because of the ambiguity and dynamic nature of natural language,the research of named entity recognition is very challenging.As an international language,English plays an important role in the fields of science and technology,finance and business.Therefore,the early named entity recognition technology is mainly based on English,which is often used to identify the names of people,places and organizations in the text.International conferences in the field of natural language processing,such as CoNLL,MUC,and ACE,have identified named entity recognition as a specific evaluation task,and the relevant research uses evaluation corpus from English-language media organizations such as the Wall Street Journal,the New York Times,and Wikipedia.The research of named entity recognition on relevant data has achieved good results.Aiming at the sparse distribution of entities in text,a model combining local and global features is proposed.The model takes a single English character as input,and uses the local feature layer composed of local attention and convolution to process the text pieceby way of sliding window to construct the corresponding local features.In addition,the self-attention mechanism is used to generate the global features of the text to improve the recognition effect of the model on long sentences.Experiments on three data sets,Resume,MSRA and Weibo,show that the proposed method can effectively improve the model’s recognition of English named entities.