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Linguistic Complexity across Two Early Modern English Scientific Text Types

Paper · 2020 · Jesús Romero-Barranco

Summary

The study asks whether linguistic complexity differs between two kinds of early medical writing in English. The question had not been examined for this period, particularly across text types. The source is a single sixteenth-century manuscript, MS Hunter 135 (Glasgow University Library). It holds two texts of almost equal length. One is a surgical treatise based on Guy de Chauliac, with additions from Henry de Mondeville and others, aimed at learned readers. The other is a collection of medical recipes intended for lay users.

The author worked in three steps:

  • transcribed the manuscript semi-diplomatically
  • normalised the spelling to Present-Day English so the text could be POS-tagged and searched automatically
  • counted the features that Biber (1992) proposed as signals of reduced or increased complexity, with counts scaled per 1,000 words

The comparison is relative: each text counts as complex or simple only against the other. The surgical treatise turned out to be more complex. It has more high-complexity markers, while the recipes have more low-complexity markers. This matters because it ties register complexity to intended readership in historical scientific writing.

Key points

  • The data come from MS Hunter 135:
    • surgical treatise: ff. 34r-73v, 19,348 words
    • medical recipes: ff. 74r-121v, 19,482 words
  • The framework is Biber (1992), who marked 33 of his 67 register features as complexity indicators. His original register analysis covered 481 texts in 23 spoken and written registers and found five dimensions.
  • Reduced complexity is captured by six features grouped under three discourse functions:
    • structural reduction
    • less specified reference
    • fragmented structure
  • The pipeline has three stages:
    • semi-diplomatic transcription, which keeps original spelling, punctuation and capitals but expands abbreviations
    • spelling normalisation followed by POS tagging
    • frequency counts normalised per 1,000 words
  • Every recipe runs through the same five parts in a set order: it opens with a title, lists its ingredients, explains how to prepare the remedy, describes how and in what dose to apply it, and closes by stating how effective it is.

Interesting ideas

  • I1. Historical spelling is first mapped to its modern form so that a standard modern POS tagger can handle the text, rather than building a period-specific tagger.
  • I2. Complexity is defined only relative to a contrast text, since a single score means little without the opposite end of the scale.
  • I3. Surface-feature counts describe the finished text, not the effort of reading it. That-deletion, contractions or an anaphoric "it" make the structure look simpler but can make it harder to decode.
  • I4. Recipes share a stable slot structure (title, ingredients, preparation, application, efficacy), which makes this text type predictable.
  • I5. Raw counts are scaled per 1,000 words, and the texts compared are of near-equal size, so frequency differences can be read directly.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (tokens): a normalisation layer placed before the en tagger could let it process archaic or nonstandard texts such as older fables and tales, while keeping the original form next to the normalised one so the mapping stays explainable.
  • I2 (learn): evaluations could always pair a target corpus with a deliberately simpler or harder control corpus, so that any measured difference is read against a baseline instead of in isolation.
  • I3 (frames): coreference and ellipsis handling should flag these cases as high-effort spots, because a short surface form may hide a reference that still has to be resolved.
  • I4 (world): procedural texts could be parsed into a recipe-like template, filling a world model with inputs, steps and expected outcomes, which would help answer "how" and "what for" questions.
  • I5 (learn): diagnostic statistics on silver data, such as rates of particular dependency relations, should be length-normalised and computed on size-matched samples before comparing genres or parser outputs.

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

 Institutions

🎓Universidad de GranadaGranada · Spain7 works · 307 citations

📚 Shelves 3 Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Literature and Literary Theory · 340 works

🧭 Topics 1 English · 1,373 works

Cite

Cite as Jesús Romero-Barranco. 2020. Linguistic Complexity across Two Early Modern English Scientific Text Types. Atlantis 42.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.28914/atlantis-2020-42.2.03

@inproceedings{romerobarranco2020linguistic,
  title = {{Linguistic Complexity across Two Early Modern English Scientific Text Types}},
  author = {Jesús Romero-Barranco},
  booktitle = {Atlantis 42},
  year = {2020},
  doi = {10.28914/atlantis-2020-42.2.03},
  url = {https://doi.org/10.28914/atlantis-2020-42.2.03}
}

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  64. Received 15 October 2019 Jesús Romero-Barranco is a member of the Department of English and German at the University of Granada. His research interests are historical linguistics, manuscript studies and corpus linguistics. He has published in journals such as Studia Neophilologica, Nordic Journal of English Studies and English Worldwide, among others. find
  65. Revised version accepted 23 March 2020 find

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