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Article published In:
10th Anniversary Issue: Engaging with LL futures
Edited by Robert Blackwood and Elana Shohamy
[Linguistic Landscape 10:4] 2024
► pp. 400–424

Artificial Intelligence and Linguistic Landscape research

Affordances, challenges & considerations

ORCID logo | Teachers College, Columbia University
This article explores applications of artificial intelligence (AI) technologies in Linguistic Landscape research. Traditionally, LL research has relied on manual data collection and analysis, often involving photographs of public signage, advertisements, and other visual language displays. However, this manual approach can present challenges, including time-consuming data collection, inconsistent data quality, and potential researcher bias. Two AI technologies in particular hold promise for addressing these challenges in LL research: computer vision (CV) and large language models (LLMs). CV automates the identification and extraction of text from images, improving data accuracy and enabling large-scale image analysis. LLMs, based on natural language processing, can detect, translate, and interpret multilingual text. This article explores the affordances and challenges of using AI technologies in LL research and discusses methods to improve data collection, enhance accuracy, and support the analysis of multilingual environments. It also raises ethical issues and limitations of the technologies.
Keywords: Linguistic Landscape, artificial intelligence, computer vision, large language models, LLM, AR glasses
Article outline
  • 1.Introduction
    • 1.1AI technologies
  • 2.Affordances of using AI technologies in LL research
    • 2.1Image recognition
      • 2.1.1Improving image quality
      • 2.1.2Identifying & coding text
      • 2.1.3Collecting and analyzing big data
    • 2.2Natural language processing (NLP)
    • 2.3Geospatial analysis
    • 2.4Example 1: Geospatial analysis of bilingual signage captured from Google Earth
    • 2.5Example 2: AI-assisted signage analysis in the NYC subway
  • 3.Future directions AI technologies in LL research
    • 3.1Predictive modeling
    • 3.2Dynamic landscapes of the future: Landscapes in Augmented Reality
  • 4.Limitations and ethical considerations of AI technologies in LL research
    • 4.1Technology limitations
    • 4.2Privacy concerns
    • 4.3Cultural sensitivity & accuracy
  • 5.Conclusion
  • References
Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 25 October 2024
DOI logo
https://doi.org/10.1075/ll.24011.vos
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