Perspectivist approaches to natural language processing: a survey.

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 pe...

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Publicado en:Language Resources & Evaluation Vol. 59; no. 2; pp. 1719 - 1747
Autores principales: Frenda, Simona, Abercrombie, Gavin, Basile, Valerio, Pedrani, Alessandro, Panizzon, Raffaella, Cignarella, Alessandra Teresa, Marco, Cristina, Bernardi, Davide
Formato: Literature Review
Publicado: Springer Nature Jun2025
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Acceso en línea:Ver este registro en EBSCOhost
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        atl: Perspectivist approaches to natural language processing: a survey.
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          Frenda, Simona
          Abercrombie, Gavin
          Basile, Valerio
          Pedrani, Alessandro
          Panizzon, Raffaella
          Cignarella, Alessandra Teresa
          Marco, Cristina
          Bernardi, Davide
        affil:
          https://ror.org/048tbm396 Department of Computer Science, University of Turin, Corso Svizzera, 185, 10149, Torino, Piemonte, Italy
          https://ror.org/04mghma93 Interaction Lab, Heriot-Watt University, The Avenue, EH14 4AS, Edinburgh, Scotland
          Alexa AI, Amazon, Amazon Development Centre Italy, Via Lugaro 15, 10126, Torino, Piemonte, Italy
      su:
        Natural language processing
        Artificial intelligence
        Machine learning
        Image processing
        Worldview
      sug:
        subj:
          Natural language processing
          Artificial intelligence
          Machine learning
          Image processing
          Worldview
      keyword:
        Annotation
        Computational models
        Disaggregated datasets
        Information and Computing Sciences Artificial Intelligence and Image Processing
        Perspectivism
        Subjectivity
      ab: 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.
      pubtype: Academic Journal
      doctype: Literature Review
      src: R
    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved.
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          year: 2025
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