Year: 2026
Type: conference-paper
Abstract: Human annotation is a foundational component of modern natural language processing (NLP).Labeled datasets underpin widely used benchmarks for sentiment analysis, toxicity detection, hate speech classification, and stance detection.Within standard NLP workflows, annotation is generally treated as a technical process aimed at recovering an objective ground truth according to predefined guidelines.This paper argues that such a view overlooks the inherently interpretive nature of annotation.Drawing on insights from sociolinguistics, discourse analysis, and cultural theory, and on a growing empirical literature on annotator subjectivity, we propose that annotation should be understood as a culturally situated interpretive practice.Annotators rely on culturally shaped norms, values, and communicative expectations when interpreting linguistic meaning, and labels in NLP datasets often reflect culturally specific interpretations rather than universal truths.We position this argument relative to recent work on perspectivism, annotator-aware modeling, and cross-cultural annotation, and we use published findings from large-scale cross-cultural annotation studies to illustrate the concrete consequences of treating annotation as objective.We close with a research agenda for culturally informed annotation practice that includes operational recommendations on documentation, modeling, and evaluation.
Author: Wajdi Zaghouani
Institution: Northwestern University in Qatar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166884827