Elamur Research⌕
☰
home / papers / 2024 / Diachronic Analysis of Multi-word Expression Functional Categories in Scientific English

Diachronic Analysis of Multi-word Expression Functional Categories in Scientific English

Summary

The authors study how the functions of multiword expressions (MWEs) in scientific English changed over more than three centuries. The data come from the Royal Society Corpus 6.0, which contains texts of the Royal Society of London from 1665–1996. MWEs were selected in two ways. The first was to parse the corpus with Stanza and select expressions labeled with the UD relation fixed. The second was the existing Academic Formulas List (AFL). Each expression was manually assigned to one of three functional categories from the Biber et al. taxonomy: expression of the author's position (stance), discourse organization, or reference. To identify periods of change, adjacent time slices were compared using relative entropy (Kullback-Leibler Divergence). In the early period, discourse and referential expressions competed with each other. Over the last hundred years, functions became more diverse, and stance expressions, above all epistemic ones, took over the leading role. The authors explain this by the fact that such expressions direct the reader's attention to evidential information. The work combines UD annotation, register theory and information-theoretic methods. This approach can be transferred to diachronic corpora of other languages.

Key points

  • Corpus: RSC 6.0 comprises 47,837 texts, approximately 296 million tokens. Its size grows sharply over time: from ~2.6 million tokens in 1665–1699 to ~172 million in 1950–1996.
  • Parsing: Stanza with a combined English model trained on EWT, GUM, GUMReddit, PUD and Pronouns. The functions of the 100 most frequent fixed expressions were annotated manually. A manual check of 70 sentences showed a LAS of at least 85% for fixed MWEs in all periods.
  • AFL: 207 core formulas, 200 expressions from written texts and 200 from spoken academic language. Selection for the list is based on the FTW metric, which combines frequency and mutual information.
  • Change-detection method: KLD between 20-year windows shifted in 5-year steps. Differences in vocabulary size are offset by Jelinek-Mercer smoothing with λ = 0.05.
  • Taxonomy: the largest subtype is expressions that specify attributes (177 MWEs, such as on the basis of). Second come epistemic stance expressions (84).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

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

@inproceedings{alves2024diachronic,
  title = {{Diachronic Analysis of Multi-word Expression Functional Categories in Scientific English}},
  author = {Alves, Diego and Degaetano-Ortlieb, Stefania and Schmidt, Elena and Teich, Elke},
  booktitle = {Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024},
  year = {2024},
  doi = {10.63317/4shjtz9wo59s},
  url = {https://doi.org/10.63317/4shjtz9wo59s}
}

References

24 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. 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, St. Julians, Malta. Association for Computational Linguistics. find
  2. Douglas Biber and Federica Barbieri. 2007. Lexical Bundles in University Spoken and Written Registers. English for specific purposes, 26(3):263– 286. ↗
  3. Douglas Biber, Susan Conrad, and Viviana Cortes. 2004. If you look at. . . : Lexical Bundles in University Teaching and Textbooks. Applied linguistics, 25(3):371–405. ↗
  4. 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, 3. ↗
  5. Kathy Conklin and Norbert Schmitt. 2012. The Processing of Formulaic Language. Annual Review of Applied Linguistics, 32:45–61. ↗
  6. Marie-Catherine De Marneffe, Christopher D. Manning, Joakim Nivre, and Daniel Zeman. 2021. Universal dependencies. Computational Linguistics, 47(2):255–308. find our card
  7. Stefania Degaetano-Ortlieb and Elke Teich. 2018. Using relative entropy for detection and analysis of periods of diachronic linguistic change. In Proceedings of the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature at COLING2018, pages 22–33, Santa Fe, New Mexico. Association for Computational Linguistics. ↗
  8. Stefania Degaetano-Ortlieb and Elke Teich. 2019. Toward an Optimal Code for Communication: The Case of Scientific English. Corpus Linguistics and Linguistic Theory, 0(0):1–33. Ahead of print. ↗
  9. Stefan Evert. 2005. The Statistics of Word Cooccurrences: Word Pairs and Collocations. Ph.D. thesis, Universität Stuttgart. ↗
  10. Peter Fankhauser, Jörg Knappen, and Elke Teich. 2014. Exploring and Visualizing Variation in Language Resources. In LREC, pages 4125–4128. find
  11. 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
  12. Stefan Th. Gries. 2022. Multi-word Units (and Tokenization More Generally): a Multi-dimensional and Largely Information-theoretic Approach. Lexis. Journal in English Lexicology, (19). ↗
  13. MAK Halliday and CMIM Matthiessen. 2014. Halliday’s Introduction to Functional Grammar, volume 17. Routledge. find
  14. Ken Hyland. 2008. As can be seen: Lexical Bundles and Disciplinary Variation. English for specific purposes, 27(1):4–21. ↗
  15. Solomon Kullback and Richard A Leibler. 1951. On Information and Sufficiency. The annals of mathematical statistics, 22(1):79–86. ↗
  16. 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
  17. David Oakey. 2020. Phrases in EAP Academic Writing Pedagogy: Illuminating Halliday’s Influence on Research and Practice, journal = Journal of English for Academic Purposes. 44:100829. ↗
  18. 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
  19. Agata Savary, Manfred Sailer, Yannick Parmentier, Michael Rosner, Victoria Rosén, Adam Przepiórkowski, Cvetana Krstev, Veronika Vincze, Beata Wójtowicz, Gyri Smørdal Losnegaard, Carla Parra Escartín, Jakub Waszczuk, Matthieu Constant, Petya Osenova, and Federico Sangati. 2015. PARSEME – PARSing and Multiword Expressions within a European Multilingual Network. In 7th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics (LTC 2015), Poznań, Poland. find
  20. Nathan Schneider, Dirk Hovy, Anders Johannsen, and Marine Carpuat. 2016. SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM). In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 546–559. find our card
  21. Rita Simpson-Vlach and Nick C. Ellis. 2010. An Academic Formulas List: New Methods in Phraseology Research. Applied linguistics, 31(4):487– 512. ↗
  22. Joshua Tanner and Jacob Hoffman. 2023. MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation. arXiv preprint arXiv:2303.06623. ↗ our card
  23. 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. ↗
  24. 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. ↗

↑ back to top

This page in Markdown →