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[1]翁玉秀.英语新闻标题词汇层面的计算文体学研究[J].集美大学学报(哲学社会科学版),2018,21(02):122-129.
 WENG Yu-xiu.A Computational Stylistic Study of English News Headlines at Lexical Level[J].philosophy&social sciences,2018,21(02):122-129.
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英语新闻标题词汇层面的计算文体学研究(PDF)
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《集美大学学报》(哲学社会科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
21
期数:
2018年02期
页码:
122-129
栏目:
出版日期:
2018-04-28

文章信息/Info

Title:
A Computational Stylistic Study of English News Headlines at Lexical Level
作者:
翁玉秀
(集美大学诚毅学院,福建 厦门 361021)
Author(s):
WENG Yu-xiu
(Chengyi College, Jimei University, Xiamen 361021, China)
关键词:
英语新闻标题计算文体学词长词汇密度词频
Keywords:
English news headline Computational Stylistics word length lexical density word frequency
分类号:
-
DOI:
-
文献标志码:
A
摘要:
词汇是语言结构的基本单位,是表达意义和实现交际目的的工具。新闻英语作为信息传播媒介,而新闻标题是对新闻信息的浓缩和概括,有其独特的用词特征。通过自建语料库,采用语料库驱动的计算文体学研究方法,量化分析新闻标题的词长、词汇密度和词频使用特征。研究发现新闻标题以短小词为主,平均词长5.13;其STTR值为63.07,表明标题词汇密度大、用词丰富多样;词频分布分析发现新闻标题中功能词常被省略、没有第一和第二人称代词、“say”词使用频繁、新闻标题词汇具有时代特征。同时本研究还分析了这些词汇特征产生的文体效果和成因。
Abstract:
Words are the basic units of languages and the tool of expressing meaning and realizing communicative purposes. News English works as the medium of information dissemination, while news headline, the condensed and generalized news information, has its unique lexical feature. Based on self-built corpus and corpus-driven approach of computational stylistic analysis, the present study reports a quantitative analysis of the lexical features of news headlines on the level of word length, lexical density and word frequency, and arrives at the following conclusions: there are mainly midget words in news headlines, which has a mean word length of 5.13; the news headline has a STTR of 63.07, which indicates its great word diversity and vocabulary richness; the study of lexical frequency finds out that function words are often omitted in news headlines, there are no first and second personal nouns in news headlines, there is a high frequency of “say” word, and words in news headlines have the characteristics of the times. The study also analyzes the stylistic effects and causes of these lexical features.

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更新日期/Last Update: 2018-05-24