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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

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Artificial Intelligence and Linguistic Landscape research

Affordances, challenges & considerations

ORCiD logo with link | Teachers College, Columbia University
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Open Access publication of this article was funded through a Transformative Agreement with Columbia University.

Published online: 25 October 2024
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https://doi.org/10.1075/ll.24011.vos
Abstract
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.
Sammanfattning
Denna artikel utforskar tillämpningar av artificiella intelligens (AI)-teknologier i forskning om språkliga landskap (LL). Traditionellt har LL-forskning förlitat sig på manuell datainsamling och analys, vilket ofta innebär fotografier av offentliga skyltar, annonser och andra visuella språkdisplayer. Men denna manuella metod kan innebära utmaningar, inklusive tidskrävande datainsamling, inkonsekvent datakvalitet och potentiell forskarbias. Två AI-teknologier är särskilt lovande för att möta dessa utmaningar inom LL-forskning: datorseende (CV) och stora språkmodeller (LLM). CV automatiserar identifieringen och extraktionen av text från bilder, förbättrar data noggrannhet och möjliggör storskalig bildanalys. LLM, som bygger på naturlig språkbehandling (NLP), kan upptäcka, översätta och tolka flerspråkig text. Denna artikel utforskar möjligheter och utmaningar med att använda AI-teknologier i LL-forskning och diskuterar metoder för att förbättra datainsamlingen, öka noggrannheten och stödja analysen av flerspråkiga miljöer. Den tar också upp etiska frågor och begränsningar med teknologierna.
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
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