Beyond semantics: the challenges of annotating pragmatic and discourse phenomena

2026-04-19 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

This special issue explores the significant challenges in reliably annotating abstract semantic and pragmatic information within natural language, spanning both sentence and discourse levels. The core difficulty arises because such information is often not explicitly or unambiguously marked, requiring annotators to reconstruct complex relations and situations based on contextual cues. The issue aims to demonstrate these annotation hurdles and present current approaches and solutions being developed to address them effectively.

Key takeaway

For research scientists developing natural language processing models, understanding the inherent ambiguity and context-dependency of pragmatic and discourse phenomena is crucial. You should prioritize developing annotation schemes and models that can effectively capture and reconstruct complex, implicit contextual information, rather than relying solely on explicit linguistic markers.

Key insights

Annotating abstract semantic and pragmatic information is challenging due to its implicit, context-dependent nature.

Principles

• Pragmatic information is context-dependent.
• Implicit meaning requires reconstruction.

Topics

• Semantic Annotation
• Pragmatic Annotation
• Discourse Phenomena
• Natural Language Annotation
• Contextual Information

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.