Diachronic Analysis of Phrasal Verbs in English Scientific Writing

What it is about

Phrasal verbs (verb plus particle, such as "carry out") are rare in scientific prose, and the paper asks whether they take part in the standardisation of scientific English, as other multi-word expressions do. The data are the Royal Society Corpus, from the mid-17th to the end of the 20th century. Three measures are used: the Kullback-Leibler divergence of each verb's distribution over time, the surprisal of the particle given the verb, and the paradigmatic variability of the verbs in word embedding spaces. The divergence stays roughly flat until the 20th century, when a conventionalisation trend appears. The surprisal shows a decrease in the 18th to mid-19th century and a peak around 1970. The paper names the verbs that drive the two diversification peaks, around 1846 and 1971. Phrasal verbs lose paradigmatic variability faster than other verbs, mainly in the 20th century.

Ideas

• I1. 19. The lexicon: vocabulary, multiword expressions and register: keep a table of verb-particle pairs with their period and register, from the paper's lists. Use it as a scored multi-word lexicon for the parser, so that the particle is attached to the verb as compound:prt in en/src/parse.rs. Measure particle attachment on UD EWT dev, run twice.
• I2. The surprisal of the particle given the verb is a count ratio over a corpus. Use it as a deterministic rule feature that decides between a phrasal and a literal reading, and test it on the paper's verb lists before any change to the grammar.
• I3. Report the paper's three measures per period as a table for the register chapter, only for corpora whose licence allows it.

For Mova

The chapters are 19. The lexicon: vocabulary, multiword expressions and register and 37. Register and variation. The code is en/src/parse.rs for the particle attachment and en/src/dict.rs for the lexicon. Measure: particle attachment accuracy on UD EWT dev, with a control in which the particle lexicon is shuffled.

Cite

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

@inproceedings{alves2025diachronic,
  title = {{Diachronic Analysis of Phrasal Verbs in English Scientific Writing}},
  author = {Alves, Diego},
  booktitle = {Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)},
  year = {2025},
  url = {https://aclanthology.org/2025.nodalida-1.2/}
}

References

15 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Stefan Fischer, Jörg Knappen, Katrin Menzel, and Elke Teich. 2020. The royal society corpus 6.0: Providing 300+ years of scientific writing for humanistic study. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 794–802. find
2. Rafael Alejo-González. 2010. Making sense of phrasal verbs: A cognitive linguistic account of l2 learning. AILA review, 23(1):50–71. ↗
3. Hasan Alisoy. 2023. Enhancing understanding of english phrasal verbs in first-year elt students through cognitive-linguistic methods. Md. ↗
4. Manjurul Hussain and Ishtiak Mahmud. 2019. py-MannKendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software, 4(39):1556. find
5. Diego Alves, Stefan Fischer, Stefania Degaetano-Ortlieb, and Elke Teich. 2024. Multi-word expres-Ken Hyland. 2008. As can be seen: Lexical bundles and disciplinary variation. English for specific purposes, 27(1):4–21. find
6. Solomon Kullback and Richard A Leibler. 1951. On Information and Sufficiency. The annals of mathematical statistics, 22(1):79–86. ↗
7. Wang Ling, Chris Dyer, Alan W Black, and Isabel Trancoso. 2015. Two/too simple adaptations of word2vec for syntax problems. In Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 1299–1304. ↗
8. Dilin Liu. 2012. The most frequently-used multiword constructions in academic written english: A multi-corpus study. English for Specific Purposes, 31(1):25–35. find
9. Al-Otaibi Ghuzayyil Mohammed. 2019. A cognitive approach to the instruction of phrasal verbs: Rudzka-ostyn’s model. Journal of Language and Education, 5(2 (18)):10–25. ↗
10. Michelle Perdomo and Edith Kaan. 2023. Surprisal effects in priming of phrasal verb construction alternations in native speakers and l2 learners. ↗
11. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. https://nlp.stanford.edu/pubs/qi2020stanza.pdf 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
12. Anna Siyanova-Chanturia and Diana Van Lancker Sidtis. 2018. What online processing tells us about formulaic language. Understanding formulaic language, pages 38–61. ↗
13. Julius Steuer, Marie-Pauline Krielke, Stefan Fischer, Stefania Degaetano-Ortlieb, Marius Mosbach, and Dietrich Klakow. 2024. Modeling diachronic change in english scientific writing over 300+ years with transformer-based language model surprisal. In Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC)@ LREC-COLING 2024, pages 12–23. ↗
14. 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, 5:620275. ↗
15. Chengxiang Zhai and John Lafferty. 2004. A Study of Smoothing Methods for Language Models Applied to Information Retrieval. ACM Transactions on Information Systems (TOIS), 22(2):179–214. ↗

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