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Language Encodes Geographical Information
Open accessAbstract
Population counts and longitude and latitude coordinates were estimated for the 50 largest cities in the United States by computational linguistic techniques and by human participants. The mathematical technique Latent Semantic Analysis applied to newspaper texts produced similarity ratings between the 50 cities that allowed for a multidimensional scaling (MDS) of these cities. MDS coordinates correlated with the actual longitude and latitude of these cities, showing that cities that are located together share similar semantic contexts. This finding was replicated using a first‐order co‐occurrence algorithm. The computational estimates of geographical location as well as population were akin to human estimates. These findings show that language encodes geographical information that language users in turn may use in their understanding of language and the world.
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Prior work has shown that natural language encodes geographic information.
“Prior work has shown that natural language encodes geographic information (Louwerse & Zwaan, 2009;Louwerse & Benesh, 2012)”
From Language Models Represent Space and Time (2023) · cites this paper as Louwerse & Zwaan, 2009
Relative coordinates can be approximately recovered with simple techniques like multidimensional scaling, co-occurrence statistics, or probing word embeddings.
“relative coordinates can be approximately recovered with simple techniques like multidimensional scaling, co-occurrence statistics, or probing word embeddings (Louwerse & Zwaan, 2009;Mikolov et al., 2013a;Gupta et al., 2015;Konkol et al., 2017).”
From Language Models Represent Space and Time (2023) · cites this paper as Louwerse & Zwaan, 2009
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Cited by
Sorted by citations
Symbol Interdependency in Symbolic and Embodied Cognition
Louwerse, M. M. (2011) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2010.01106.x · Cited by 399
Self-citationLanguage Models Represent Space and Time
Gurnee, W., & Tegmark, M. (2023) · arXiv · doi:10.48550/arxiv.2310.02207 · Cited by 46
Maps and Space Are Entangled with Language Experience
Rinaldi, L., & Marelli, M. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.07.009 · Cited by 18
Metadata
- Authors
- Louwerse, M. M., & Zwaan, R. A.
- Affiliations
- Erasmus University Rotterdam (NL)
- Corresponding author
- Louwerse, Max M.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2009-01
- Volume
- 33
- Issue
- 1
- Pages
- 51-73
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2131829566
- Cited by
- 113
- References
- 3
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 7 · 2023: 7 · 2022: 8 · 2021: 7 · 2020: 27 · 2019: 5 · 2018: 4 · 2017: 4 · 2016: 3 · 2015: 8 · 2014: 9 · 2013: 4 · 2012: 6
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- National Science Foundation (0416128)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Keywords
- population estimates, latent semantic analysis, newspaper texts, multidimensional scaling, word co-occurrence, semantic similarity, computational linguistics
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