Enhanced Evaluative Language Annotation through Refined Theoretical Framework and Workflow

As precursor work in preparation for an international standard ISO/PWI 24617-16 Language resource management – Semantic annotation – Part 16: Evaluative language, we aim to test and enhance the reliability of the annotation of subjective evaluation based on Appraisal Theory. We describe a comprehensive three- phase workflow tested on COVID-19 media reports to achieve reliable agreement through progressive training and quality control. Our methodology addresses some of the key challenges through the refinement of targeted guideline refinements and the development of inter- active clarification tools, alongside a custom platform that enables the pre-classification of six evaluative categories, systematic annotation review, and organized documentation. We report empirical results that demonstrate substantial improvements from the initial moderate agreement to a strong final consensus. Our research offers both theoretical refinements addressing persistent classification challenges in evaluation and practical solutions for the implementation of the annotation workflow, proposing a replicable methodology for the achievement of reliable annotation consistency in the annotation of evaluative language.

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CityU Scholars
Published
2025-09-24
Primary Topic
Natural Language Processing Techniques
Type
article
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article

Enhanced Evaluative Language Annotation through Refined Theoretical Framework and Workflow

Jianfang Zong, James Pustejovsky, Ki Yong Lee, Yangli Jia et al.
CityU Scholars
Natural Language Processing Techniques
article

Enhanced Evaluative Language Annotation through Refined Theoretical Framework and Workflow

Jianfang Zong, James Pustejovsky, Ki Yong Lee, Yangli Jia, Laurent Romary, Harry Bunt, Sylviane Cardey, Shengqing Liao, Xinyu Cao, Min Dong, Haitao Wang, Jingtao Zeng, Clark Alex, Tianyong Hao, François-Claude Rey
article en

Abstract

As precursor work in preparation for an international standard ISO/PWI 24617-16 Language resource management – Semantic annotation – Part 16: Evaluative language, we aim to test and enhance the reliability of the annotation of subjective evaluation based on Appraisal Theory. We describe a comprehensive three- phase workflow tested on COVID-19 media reports to achieve reliable agreement through progressive training and quality control. Our methodology addresses some of the key challenges through the refinement of targeted guideline refinements and the development of inter- active clarification tools, alongside a custom platform that enables the pre-classification of six evaluative categories, systematic annotation review, and organized documentation. We report empirical results that demonstrate substantial improvements from the initial moderate agreement to a strong final consensus. Our research offers both theoretical refinements addressing persistent classification challenges in evaluation and practical solutions for the implementation of the annotation workflow, proposing a replicable methodology for the achievement of reliable annotation consistency in the annotation of evaluative language.

CityU Scholars
City University of Hong Kong (HK), South China Normal University (CN), Tilburg University (NL), Liaocheng University (CN), Korea University (KR), Fudan University (CN), Sorbonne Université (FR), China National Institute of Standardization (CN), Université Marie et Louis Pasteur (FR), Brandeis University (US), Beihang University (CN)
City University of Hong Kong, National Office for Philosophy and Social Sciences
Openalex Percentile: Top 82%
Natural Language Processing Techniques
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