Analysis on Features of News Headlines Translation

This paper is holding the purpose of summing up the theories and methods based on many examples which demonstrate principles,features and technique skills in news headline translation.We know that the translation of news headlines which is considered as the eyes and window of news appears very essential as many people do not have enough time to read the whole text from beginning to the end.They tend to browse headlines at first and then pick up the news in which the headline attracts them.From the paper,we can draw a conclusion that English news headline translation is not an easy task,and we need more study and practice to make it better and do our utmost to help Chinese readers when they are reading English newspapers or magazines.

Roman Urdu News Headline Classification Empowered with Machine Learning

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent.Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing.The communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply.English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the past.This is due to the numerous complexities involved in the processing of Roman Urdu data.The complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured format.We have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be done.The author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different websites.The preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the news.After applying above mentioned different machine learning algorithms,results have shown that Multinomial Naïve Bayes classifier is giving the best accuracy of 90.17%which is due to the noise lexical variation.

A Study of Stylistic Features of English News Headlines

From different aspects we can draw out different characteristics of news headlines. According to various study and research from different authors, we can conclude their characteristics are displayed on syntactical level, punctuation level, lexical level, rhetorical level and cultural level.

An Analysis of English News Headlines

A headline is an indispensable component of a news report and is the eye of the pages on the newspapers,through which the readers can gain as much information as possible in the newspaper within the shortest time and search for clues to stories that interest them,as the headline is the condensation of the whole story.Despite their own characteristic ways of editing headlines,English newspapers share something in common,namely brevity,accuracy,clarity and eye-catchingness.However,these features unique to English newspaper headlines constitute a big barrier to readers' understanding of the headlines and they may become quite at loss when reading headlines at the very beginning.This thesis aims to make an analysis of English news headlines in terms of their features at structural,lexical and rhetorical levels and hopes to help readers understand English news headline better.

Translation of News Headlines Under Contextual Theory

With the coming of all kinds of information,the world is becoming one indivisible planet.People not only pay attention to major events in their own countries,but also gradually pay attention to international news and learn about the world situation.As one of the world’s common languages,English news accounts for a large proportion of the news market.Usually when people choose to read current events,they will look at the headlines first,and then at the content of the article.Therefore,whether the translated news headlines can accurately convey information and catch readers’attention in a short time plays a very important role in the dissemination of news.Based on the context theory,this paper studies and analyzes the translation of some news headlines,expecting to provide some suggestions for news translation.

Studyon the E-C Translation of Financial NewsHeadlines inthe Context of the Skopos Theory

As the heart of news,financial news headline determines whetherpeople are interested in the content.The purpose of translating financial news headlines is to accurately convey the financial information of foreign media to domestic readers and attract domestic readers’attention by means of rhetoric,typesetting,and so on.This paper mainly discusses how the Skopos Theory is applied in translating financial news headlines through case studies.In the context of Skopos Theory,translation principles for different financial news headlines will also be explored and summarized.