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Hedging in Scientific Research Articles

Summary

The book is a corpus-based study of hedging, the linguistic means by which writers soften or qualify their claims, in scientific research articles. It argues that hedges are not signs of vagueness or weakness but an important rhetorical resource: they mark the writer's degree of commitment, anticipate objections and show respect for the reader and the research community. The author analyses which forms realise hedging (modal verbs, epistemic lexical verbs such as suggest or appear, adverbs such as perhaps or possibly, adjectives and certain constructions) and how frequently they appear. He also proposes a functional model that distinguishes hedges oriented towards the content (accuracy of the claim) from hedges oriented towards the writer and reader. The work became a standard reference for hedge lexicons in later computational research on uncertainty and hedge detection.

How Mova uses it

Effectiveness in Mova

The cue list is not ablated separately, so the contribution of this source is not measured on its own. The hedge field as a whole (from the prag README), five-fold cross-validation on 2,986 sentences, accuracy / macro-F1 %: majority baseline 97.3 / 49.3, perceptron 98.1 / 76.3, IGTree 98.0 / 75.5, k-NN 97.8 / 73.4. On the held-out test (538 sentences): baseline 96.8 / 49.2, IGTree best at 98.5 / 84.2. Shuffled-label controls stay at baseline level.


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Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1075/pbns.54

@inproceedings{hyland1998hedging,
  title = {{Hedging in Scientific Research Articles}},
  author = {Hyland, Ken},
  booktitle = {John Benjamins Publishing Company (book)},
  year = {1998},
  doi = {10.1075/pbns.54},
  url = {https://doi.org/10.1075/pbns.54}
}

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