Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology

Federico Ruggeri; Eleonora Misino; Arianna Muti; Katerina Korre; Paolo Torroni; Alberto Barrón-Cedeño

doi:10.1162/tacl.a.804

Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology

Federico Ruggeri, Eleonora Misino, Arianna Muti, Katerina Korre, Paolo Torroni, Alberto Barrón-Cedeño

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Abstract

We introduce Guideline-Centered Annotation Methodology (GCAM), a novel methodology designed to report the annotation guidelines associated with each data instance. GCAM addresses four key limitations of the standard application of the prescriptive annotation methodology by reducing the information loss during annotation, ensuring adherence to guidelines, and enabling the efficient reuse of annotated data across multiple tasks that rely on the same guidelines. We evaluate GCAM with a focus on text classification tasks through (i) a human annotation study and (ii) an experimental evaluation with several machine learning models. Our results highlight the advantages of GCAM from multiple perspectives, guaranteeing a transparent evaluation of the successful application of the prescriptive paradigm and enabling a fine-grained model error analysis.

Anthology ID:: 2026.tacl-1.107
Volume:: Transactions of the Association for Computational Linguistics, Volume 14
Month:
Year:: 2026
Address:: Cambridge, MA
Venue:: TACL
SIG:
Publisher:: MIT Press
Note:
Pages:: 2337–2356
Language:
URL:: https://aclanthology.org/2026.tacl-1.107/
DOI:: 10.1162/tacl.a.804
Bibkey:
Cite (ACL):: Federico Ruggeri, Eleonora Misino, Arianna Muti, Katerina Korre, Paolo Torroni, and Alberto Barrón-Cedeño. 2026. Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology. Transactions of the Association for Computational Linguistics, 14:2337–2356.
Cite (Informal):: Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology (Ruggeri et al., TACL 2026)
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PDF:: https://aclanthology.org/2026.tacl-1.107.pdf

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@article{ruggeri-etal-2026-guidelines,
    title = "Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology",
    author = "Ruggeri, Federico  and
      Misino, Eleonora  and
      Muti, Arianna  and
      Korre, Katerina  and
      Torroni, Paolo  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "14",
    year = "2026",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2026.tacl-1.107/",
    doi = "10.1162/tacl.a.804",
    pages = "2337--2356",
    abstract = "We introduce Guideline-Centered Annotation Methodology (GCAM), a novel methodology designed to report the annotation guidelines associated with each data instance. GCAM addresses four key limitations of the standard application of the prescriptive annotation methodology by reducing the information loss during annotation, ensuring adherence to guidelines, and enabling the efficient reuse of annotated data across multiple tasks that rely on the same guidelines. We evaluate GCAM with a focus on text classification tasks through (i) a human annotation study and (ii) an experimental evaluation with several machine learning models. Our results highlight the advantages of GCAM from multiple perspectives, guaranteeing a transparent evaluation of the successful application of the prescriptive paradigm and enabling a fine-grained model error analysis."
}

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Download as File

%0 Journal Article
%T Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology
%A Ruggeri, Federico
%A Misino, Eleonora
%A Muti, Arianna
%A Korre, Katerina
%A Torroni, Paolo
%A Barrón-Cedeño, Alberto
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F ruggeri-etal-2026-guidelines
%X We introduce Guideline-Centered Annotation Methodology (GCAM), a novel methodology designed to report the annotation guidelines associated with each data instance. GCAM addresses four key limitations of the standard application of the prescriptive annotation methodology by reducing the information loss during annotation, ensuring adherence to guidelines, and enabling the efficient reuse of annotated data across multiple tasks that rely on the same guidelines. We evaluate GCAM with a focus on text classification tasks through (i) a human annotation study and (ii) an experimental evaluation with several machine learning models. Our results highlight the advantages of GCAM from multiple perspectives, guaranteeing a transparent evaluation of the successful application of the prescriptive paradigm and enabling a fine-grained model error analysis.
%R 10.1162/tacl.a.804
%U https://aclanthology.org/2026.tacl-1.107/
%U https://doi.org/10.1162/tacl.a.804
%P 2337-2356

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Markdown (Informal)

[Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology](https://aclanthology.org/2026.tacl-1.107/) (Ruggeri et al., TACL 2026)

Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology (Ruggeri et al., TACL 2026)

ACL

Federico Ruggeri, Eleonora Misino, Arianna Muti, Katerina Korre, Paolo Torroni, and Alberto Barrón-Cedeño. 2026. Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology. Transactions of the Association for Computational Linguistics, 14:2337–2356.