Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency. Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load. The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years. We base our observations on five dependency parsers, including the widely used Stanford CoreNLP as well as 4 newer alternatives. Our analysis of syntactic language change goes beyond linear dependency distance and explores 15 metrics relevant to dependency distance minimization (DDM) and/or based on tree graph properties, such as the tree height and degree variance. Even though we have evidence that recent parsers trained on modern treebanks are not heavily affected by data 'noise' such as spelling changes and OCR errors in our historic data, we find that results of syntactic language change are sensitive to the parsers involved, which is a caution against using a single parser for evaluating syntactic language change as done in previous work. We also show that syntactic language change over the time period investigated is largely similar between English and German across the different metrics explored: only 4% of cases we examine yield opposite conclusions regarding upwards and downtrends of syntactic metrics across German and English. We also show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions. To our best knowledge, ours is the most comprehensive analysis of syntactic language using modern NLP technology in recent corpora of English and German.

基于英语和德语的议会辩论文献，本研究使用五个依赖解析器对句法语言变化进行历时趋势分析，包括线性依赖距离和其他与依赖距离最小化相关的度量标准，结果表明在不同的度量标准下，句法语言变化在英语和德语中具有类似趋势，且变化更频繁出现在句子长度分布的两端，这是一项使用现代自然语言处理技术的英语和德语句法语言的最广泛分析。

英语和德语中的句法语言变化：指标、分析器和趋同性