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Temporal Analysis of Language through Neural Language Models
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We provide a method for automatically detecting change in language across time through a chronologically trained neural language model. We train the model on the Google Books Ngram corpus to obtain word vector representations specific to each year, and identify words that have changed significantly from 1900 to 2009. The model identifies words such as "cell" and "gay" as having changed during that time period. The model simultaneously identifies the specific years during which such words underwent change.
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“Rodman (2020) tests four different methods by comparing results to the gold standard; ultimately, the chronologically trained model (Kim et al. 2014) is the best performer.”
From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Kim et al. 2014
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Design and implementation of ShiCo : Visualising shifting concepts over time
Martínez-Ortiz, C., Kenter, T., Wevers, M., Huijnen, P., Verheul, J., & van Eijnatten, J. (2016) · UvA-DARE (University of Amsterdam) · Cited by 13
The Labeled Segmentation of Printed Books
McConnaughey, L., Dai, J., & Bamman, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1077 · Cited by 13
Metadata
- Authors
- Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., & Petrov, S.
- Affiliations
- New York University (US)
- University of Michigan (US)
- Google (United States) (US)
- Venue
- Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science
- Published
- 2014
- Pages
- 61-65
- OpenAlex
- W2102343050
- Cited by
- 305
- References
- 16
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 15 · 2023: 21 · 2022: 28 · 2021: 42 · 2020: 60 · 2019: 53 · 2018: 24 · 2017: 22 · 2016: 18 · 2015: 9 · 2014: 1
- FWCI
- 9.82
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- word embeddings, word vector representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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