Is dependency distance experiencing a process of minimization? A diachronic study based on the State of the Union addresses

• Published in: Lingua Vol. 239; p. 102762
• Main Authors: Lei, Lei, Wen, Ju
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-05-2020; Elsevier Science Ltd
• Subjects: Algorithms; Dependency distance; Diachronic analysis; Diachronic exploration; Mean dependency distance; Minimization; Normalized dependency distance; Political discourse; Presidents; Short term memory; Speeches; Syntactic complexity; United States > US; Normalized dependency distance; Minimization; Mean dependency distance; Diachronic exploration; Dependency distance
• ISSN: 0024-3841; 1872-6135
• DOI: 10.1016/j.lingua.2019.102762

⿢An initial diachronic study to investigate dependency distance minimization.⿢Dependency distance has been decreasing across the examined span of more than 200 years.⿢Dependency distance tends to minimize due to the constraint of working memory capacity.⿢The principle of least effort governs the preference for shorter dependency distance.
Dependency distance has been considered a valid measure of syntactic complexity, and it has been extensively investigated in various contexts. One interesting line of research focuses on the issue of dependency distance minimization. Studies have shown that dependency distance has been experiencing a process of minimization in order to adapt to the limited resource of human working memory. However, little is known of whether dependency distance also tends to minimize across a long span of time. If dependency distance tends to minimize, then it should be hypothesized that the minimization process may also occur across a long span of time. To this end, this study investigates the possible change of dependency distance in a diachronic dataset, i.e., the State of the Union addresses from 1790 to 2017. Results of the study confirm the hypothesis that dependency distance has been decreasing across the examined 200 years. Besides, the correlation of mean dependency distance and normalized dependency distance suggests that these two algorithms may serve well as measures of the same linguistic construct, i.e. syntactic complexity.