英语新闻依存距离特征
Dependency Distance Features of English News
DOI: 10.12677/ml.2026.141064, PDF,   
作者: 李哲先：上海海事大学外国语学院，上海
关键词: 标准化依存距离；新闻主题；内容类型；Normalized Dependency Distance； News Topics； Content Types
摘要: 依存距离(DD)作为量化句法复杂度的指标，已在跨语言和跨体裁比较研究中得到广泛应用，但目前缺乏对单一语类，特别是英语新闻中不同主题和内容类型句子的依存距离分析。本研究选取了Choubey (2020)构建的News Discourse语料库，使用Python的Spacy库进行依存句法分析，比较了英语新闻中不同主题(政治、商业、灾难和犯罪)和内容类型(事件、背景、评价、传闻等)句子的标准化依存距离(NDD)。研究发现：1) 结果显示，商业、政治主题新闻句子的标准化依存距离差异较大，而犯罪和灾难新闻句子的标准化依存距离差异较小。2) 讲述主要事件和未来展望的句子NDD值最高，而传闻类句子的NDD值最低。本研究通过将依存距离指标应用于新闻体裁内部，填补了现有研究空白，并提出NDD或可作为衡量新闻翻译记忆负荷的有效量化指标，对翻译实践与教学具有启示意义。
Abstract: Dependency distance (DD), a quantitative index of syntactic complexity, has been widely applied in cross-linguistic and cross-genre comparative research. However, analyses within a single genre remain scarce, particularly with respect to how dependency distance varies across topics and content types in English news. Drawing on the News Discourse corpus compiled by Choubey (2020), this study conducts dependency parsing using spaCy in Python and compares normalized dependency distance (NDD) across four news topics (politics, business, disaster, and crime) and multiple content types (e.g., event, background, evaluation, and rumor). The results show that sentences in business and political news display greater variation in NDD, whereas sentences in crime and disaster news exhibit relatively smaller differences in NDD. In addition, sentences describing the main event and future outlook yield the highest NDD values, while rumor sentences yield the lowest. By applying dependency distance to an intra-genre analysis of news discourse, this study helps fill a gap in the literature and suggests that NDD may serve as a useful quantitative proxy for estimating memory demands in news translation, with implications for translation practice and pedagogy.
文章引用：李哲先. 英语新闻依存距离特征[J]. 现代语言学, 2026, 14(1): 491-497. https://doi.org/10.12677/ml.2026.141064

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