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Tracing Syntactic Complexity: Exploring the Evolution of Average Dependency Length Across Three Centuries of Scientific English

What it is about

The question is whether average dependency length (aDL) per sentence, which earlier work found to fall, keeps falling in scientific English. The authors extend the Royal Society Corpus to cover 1665 to 1996, with about 129 million tokens, and parse it with Stanza into UD. The average falls until the 19th century and then rises significantly in the 20th century. A fine-grained analysis points to multi-word compounds as the main source of long leftward noun phrases. On a manually corrected sample of 140 sentences, the parser reaches a UAS of 91.06 overall. Counting compounds as single words changes the aDL figures visibly.

Ideas

For Mova

The code is en/src/ctx.rs and the command en ud-eval; the chapters are 18. Compounding, conversion and the minor processes and 21-clause-structure.md. The paper's parser is Stanza, not en. The Royal Society Corpus is not in our licence list, so no text from it enters the build.

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{krielke2025tracing,
  title = {{Tracing Syntactic Complexity: Exploring the Evolution of Average Dependency Length Across Three Centuries of Scientific English}},
  author = {Krielke, Marie-Pauline and Alves, Diego and Talamo, Luigi},
  booktitle = {Proceedings of the Eighth International Conference on Dependency Linguistics (Depling, SyntaxFest 2025)},
  year = {2025},
  url = {https://aclanthology.org/2025.depling-1.2/}
}

References

47 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. the phrasal and the causal level Biber and Gray (2016, p.62), and both affect aDL. Hence, phrasal compression through premodification of the nominal head does not necessarily minimize aDL but can rather lead to aDL expansion if used excessively. As future work, we intend to go beyond DL to analyze trade-offs in syntactic complexity over time (e.g., tree depth, intervener complexity), and to incorporate measures from constituency-parsed corpora (e.g., average branching factor). find
  2. Diego Alves, Stefan Fischer, Stefania Degaetano-Ortlieb, and Elke Teich. 2024. Multi-word expressions in English scientific writing. In Proceedings of the 8th joint SIGHUM workshop on computational linguistics for cultural heritage, social sciences, humanities and literature (LaTeCH-CLfL 2024), pages 67–76. ↗
  3. David Banks. 2008. The development of scientific writing: Linguistic features and historical context. Equinox. ↗
  4. Alistair Baron and Paul Rayson. 2008. VARD 2: A tool for dealing with spelling variation in historical corpora. In Proceedings of the Postgraduate Conference in Corpus Linguistics. find our card
  5. Douglas Biber and Victoria Clark. 2002. Historical shifts in modification patterns with complex noun phrase structures. Teresa Fanego, Maria López—Couso and Javier Perez—Guerra (eds.). English Historical Morphology. Selected Papers from, 11:43–66. find
  6. Douglas Biber and Bethany Gray. 2010. Challenging stereotypes about academic writing: Complexity, elaboration, explicitness. Journal of English for Academic Purposes, 9(1):2–20. ↗
  7. Douglas Biber and Bethany Gray. 2011. Grammatical change in the noun phrase: the influence of written language use. English Language and Linguistics, 15(2):223–250. ↗
  8. Douglas Biber and Bethany Gray. 2016. Grammatical Complexity in Academic English: Linguistic Change in Writing. Studies in English Language. Cambridge University Press. find
  9. Yuri Bizzoni, Stefania Degaetano-Ortlieb, Peter Fankhauser, and Elke Teich. 2020. Linguistic Variation and Change in 250 years of English Scientific Writing: A Data-driven Approach. Frontiers in Artificial Intelligence, section Language and Computation. ↗
  10. Edward Gibson, Richard Futrell, Steven P. Piantadosi, Isabelle Dautriche, Kyle Mahowald, Leon Bergen, and Roger Levy. 2019. How Efficiency Shapes Human Language. Trends in Cognitive Sciences, 23(5):389–407. ↗
  11. Jinho D Choi, Joel Tetreault, and Amanda Stent. 2015. It depends: Dependency parser comparison using a web-based evaluation tool. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 387–396. ↗ our card
  12. Daniel Gildea and David Temperley. 2010. Do Grammars Minimize Dependency Length? Cognitive Science, 34(2):286–310. ↗
  13. Kristina Gulordava, Paola Merlo, and Benoit Crabbé. 2015. Dependency length minimisation effects in short spans: a large-scale analysis of adjective placement in complex noun phrases. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 477–482. Association for Computational Linguistics. ↗
  14. Marie-Catherine de Marneffe, Christopher D. Manning, Joakim Nivre, and Daniel Zeman. 2021. Universal Dependencies. Computational Linguistics, 47(2):255–308. find our card
  15. Stefania Degaetano-Ortlieb. 2021. Measuring informativity: The rise of compounds as informationally dense structures in 20th-century scientific English. In Corpus-based Approaches to Register Variation, pages 291–312. John Benjamins Publishing Company. ↗
  16. Michael A. K. Halliday. 1988. On the language of physical science. In Mohsen Ghadessy, editor, Registers of written English: Situational factors and linguistic features, pages 162–177. Pinter. find
  17. Stefania Degaetano-Ortlieb, Hannah Kermes, Ashraf Khamis, and Elke Teich. 2016. An information-theoretic approach to modeling diachronic change in scientific English. In Carla Suhr, Terttu Nevalainen, and Irma Taavitsainen, editors, Selected papers from VARIENG – from data to evidence (d2e), Language and computers. Brill. find
  18. Michael A.K. Halliday and James R. Martin. 1993. Writing science: Literacy and discursive power. Falmer Press. find
  19. John A. Hawkins. 1994. A performance theory of order and constituency. Cambridge University Press. ↗
  20. Vera Demberg and Frank Keller. 2008. Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109(2):193–210. ↗
  21. John A. Hawkins. 2004. Efficiency and Complexity in Grammars. Oxford University Press. ↗
  22. Andrew Thomas Dyer. 2023. Revisiting dependency length and intervener complexity minimisation on a parallel corpus in 35 languages. In Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 110–119. ↗
  23. Marianne Hundt, David Denison, and Gerold Schneider. 2012. Relative complexity in scientific discourse. English Language and Linguistics, 16(2):209–240. ↗
  24. Tom S Juzek, Marie-Pauline Krielke, and Elke Teich. 2020. Exploring diachronic syntactic shifts with dependency length: the case of scientific English. In Proceedings of the fourth workshop on universal dependencies (UDW 2020), pages 109–119. find our card
  25. Stefan Fischer, Katrin Menzel, Jörg Knappen, and Elke Teich. 2020. The Royal Society Corpus 6.0 providing 300+ years of scientific writing for humanistic study. In Proceedings of the conference on language resources and evaluation (LREC). find
  26. Dan Klein and Christopher D Manning. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st annual meeting of the association for computational linguistics, pages 423–430. ↗ our card
  27. Richard Futrell. 2019. Information-theoretic locality properties of natural language. In Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019), pages 2–15, Paris, France. Association for Computational Linguistics. ↗
  28. Marie-Pauline Krielke. 2024. Cross-linguistic Dependency Length Minimization in scientific language: Syntactic complexity reduction in English and German in the Late Modern period. Languages in Contrast, 24(1):133–163. ↗
  29. Richard Futrell, Kyle Mahowald, and Edward Gibson. 2015. Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences, 112(33):10336–10341. ↗
  30. Marie-Pauline Krielke, Luigi Talamo, Mahmoud Fawzi, and Jörg Knappen. 2022. Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4808– 4816. European Language Resources Association. ↗ our card
  31. Edward Gibson. 1998. Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1):1–76. ↗
  32. Edward Gibson. 2000. The dependency locality theory: A distance-based theory of linguistic complexity. Image, language, brain, pages 95–126. ↗
  33. Lei Lei and Ju Wen. 2020. Is dependency distance experiencing a process of minimization? A diachronic study based on the State of the Union addresses. Lingua, 239:102762. find
  34. Himanshu Yadav, Ashwini Vaidya, and Samar Husain. 2017. Understanding constraints on non-projectivity using novel measures. In Proceedings of the fourth international conference on dependency linguistics (depling 2017), pages 276–286. find
  35. Haitao Liu, Chunshan Xu, and Junying Liang. 2017. Dependency distance: A new perspective on syntactic patterns in natural languages. Physics of Life Reviews, 21:171–193. ↗
  36. George Kingsley Zipf. 1949. Human behavior and the principle of least effort: An introduction to human eoclogy. Addison-Wesley Press. find
  37. Xueying Liu, Haoran Zhu, and Lei Lei. 2022. Dependency distance minimization: a diachronic exploration of the effects of sentence length and dependency types. Humanities and Social Sciences Communications, 9(1):1–9. ↗
  38. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A Python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. ↗ our card
  39. Jesús Romero-Barranco. 2020. Linguistic Complexity across Two Early Modern English Scientific Text Types. Atlantis, 42(2):50–71. ↗
  40. Helmut Schmid. 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of the International Conference on New Methods in Language Processing, pages 44–49. find
  41. Milan Straka and Jana Straková. 2017. Tokenizing, pos tagging, lemmatizing and parsing ud 2.0 with UDpipe. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 88–99. ↗ our card
  42. Elke Teich, Peter Fankhauser, Stefania Degaetano-Ortlieb, and Yuri Bizzoni. 2021. Less is More/More Diverse: On The Communicative Utility of Linguistic Conventionalization. Frontiers in Communication, 5:142. ↗
  43. David Temperley. 2008. Dependency-length minimization in natural and artificial languages. Journal of Quantitative Linguistics, 15(3):256–282. ↗
  44. David Temperley and Daniel Gildea. 2018. Minimizing syntactic dependency lengths: Typological/cognitive universal? 4(1):67–80. ↗
  45. Harry Tily. 2010. The role of processing complexity in word order variation and change. find
  46. Gui Wang, Hui Wang, Xinyi Sun, Nan Wang, and Li Wang. 2023. Linguistic complexity in scientific writing: A large-scale diachronic study from 1821 to 1920. Scientometrics, 128(1):441–460. ↗
  47. Yaqin Wang and Haitao Liu. 2017. The effects of genre on dependency distance and dependency direction. Language Sciences, 59:135–147. ↗

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