Perspectivist approaches to natural language processing : a survey
- Author
- Simona Frenda, Gavin Abercrombie, Valerio Basile, Alessandro Pedrani, Raffaella Panizzon, Alessandra Teresa Cignarella (UGent) , Cristina Marco and Davide Bernardi
- Organization
- Abstract
- In Artificial Intelligence research, perspectivism is an approach to machine learning that aims at leveraging data annotated by different individuals in order to model varied perspectives that influence their opinions and world view. We present the first survey of datasets and methods relevant to perspectivism in Natural Language Processing (NLP). We review datasets in which individual annotator labels are preserved, as well as research papers focused on analysing and modelling human perspectives for NLP tasks. Our analysis is based on targeted questions that aim to surface how different perspectives are taken into account, what the novelties and advantages of perspectivist approaches/methods are, and the limitations of these works. Most of the included works have a perspectivist goal, even if some of them do not explicitly discuss perspectivism. A sizeable portion of these works are focused on highly subjective phenomena in natural language where humans show divergent understandings and interpretations, for example in the annotation of toxic and otherwise undesirable language. However, in seemingly objective tasks too, human raters often show systematic disagreement. Through the framework of perspectivism we summarize the solutions proposed to extract and model different points of view, and how to evaluate and explain perspectivist models. Finally, we list the key concepts that emerge from the analysis of the sources and several important observations on the impact of perspectivist approaches on future research in NLP.</jats:p>
- Keywords
- NLP, Computational Linguistics, Perspectivism, Irony Detection, Subjectivity, Disaggregated datasets, Computational models, Annotation
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JFFRBP1S7QCCJ1PVEQS5P8S3
- MLA
- Frenda, Simona, et al. “Perspectivist Approaches to Natural Language Processing : A Survey.” LANGUAGE RESOURCES AND EVALUATION, 2025, doi:10.1007/s10579-024-09766-4.
- APA
- Frenda, S., Abercrombie, G., Basile, V., Pedrani, A., Panizzon, R., Cignarella, A. T., … Bernardi, D. (2025). Perspectivist approaches to natural language processing : a survey. LANGUAGE RESOURCES AND EVALUATION. https://doi.org/10.1007/s10579-024-09766-4
- Chicago author-date
- Frenda, Simona, Gavin Abercrombie, Valerio Basile, Alessandro Pedrani, Raffaella Panizzon, Alessandra Teresa Cignarella, Cristina Marco, and Davide Bernardi. 2025. “Perspectivist Approaches to Natural Language Processing : A Survey.” LANGUAGE RESOURCES AND EVALUATION. https://doi.org/10.1007/s10579-024-09766-4.
- Chicago author-date (all authors)
- Frenda, Simona, Gavin Abercrombie, Valerio Basile, Alessandro Pedrani, Raffaella Panizzon, Alessandra Teresa Cignarella, Cristina Marco, and Davide Bernardi. 2025. “Perspectivist Approaches to Natural Language Processing : A Survey.” LANGUAGE RESOURCES AND EVALUATION. doi:10.1007/s10579-024-09766-4.
- Vancouver
- 1.Frenda S, Abercrombie G, Basile V, Pedrani A, Panizzon R, Cignarella AT, et al. Perspectivist approaches to natural language processing : a survey. LANGUAGE RESOURCES AND EVALUATION. 2025;
- IEEE
- [1]S. Frenda et al., “Perspectivist approaches to natural language processing : a survey,” LANGUAGE RESOURCES AND EVALUATION, 2025.
@article{01JFFRBP1S7QCCJ1PVEQS5P8S3,
abstract = {{In Artificial Intelligence research, perspectivism is an approach to machine learning that aims at leveraging data annotated by different individuals in order to model varied perspectives that influence their opinions and world view. We present the first survey of datasets and methods relevant to perspectivism in Natural Language Processing (NLP). We review datasets in which individual annotator labels are preserved, as well as research papers focused on analysing and modelling human perspectives for NLP tasks. Our analysis is based on targeted questions that aim to surface how different perspectives are taken into account, what the novelties and advantages of perspectivist approaches/methods are, and the limitations of these works. Most of the included works have a perspectivist goal, even if some of them do not explicitly discuss perspectivism. A sizeable portion of these works are focused on highly subjective phenomena in natural language where humans show divergent understandings and interpretations, for example in the annotation of toxic and otherwise undesirable language. However, in seemingly objective tasks too, human raters often show systematic disagreement. Through the framework of perspectivism we summarize the solutions proposed to extract and model different points of view, and how to evaluate and explain perspectivist models. Finally, we list the key concepts that emerge from the analysis of the sources and several important observations on the impact of perspectivist approaches on future research in NLP.</jats:p>}},
author = {{Frenda, Simona and Abercrombie, Gavin and Basile, Valerio and Pedrani, Alessandro and Panizzon, Raffaella and Cignarella, Alessandra Teresa and Marco, Cristina and Bernardi, Davide}},
issn = {{1574-020X}},
journal = {{LANGUAGE RESOURCES AND EVALUATION}},
keywords = {{NLP,Computational Linguistics,Perspectivism,Irony Detection,Subjectivity,Disaggregated datasets,Computational models,Annotation}},
language = {{eng}},
pages = {{28}},
title = {{Perspectivist approaches to natural language processing : a survey}},
url = {{http://doi.org/10.1007/s10579-024-09766-4}},
year = {{2025}},
}
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