Temporal Analysis of Language through Neural Language Models
Open accessAbstract
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) tested four different methods by comparing results to the gold standard, and the chronologically trained model (Kim et al. 2014) was the best performer.
“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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Tahmasebi, N., & Risse, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67008-9_20 · Cited by 14 · see citing works
Follow the leader: Documents on the leading edge of semantic change get more citations
Soni, S., Lerman, K., & Eisenstein, J. (2020) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24421 · Cited by 14 · see citing works
Predicting environmental features by learning spatiotemporal embeddings from social media
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2019) · Ecological Informatics · doi:10.1016/j.ecoinf.2019.101031 · Cited by 14 · see citing works
Structured Embedding Models for Grouped DataPreprint
Rudolph, M., Ruiz, F., Athey, S. C., & Blei, D. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.10367 · Cited by 14 · see citing works
Challenges for Computational Lexical Semantic ChangePreprint
Hengchen, S., Tahmasebi, N., Schlechtweg, D., & Dubossarsky, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07668 · Cited by 15 · see citing works
Opinions are Made to be Changed: Temporally Adaptive Stance ClassificationPreprint
Alkhalifa, R., Kochkina, E., & Zubiaga, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12476 · Cited by 14 · see citing works
Tracing metaphors in time through self-distance in vector spaces
Tredici, M. D., Nissim, M., & Zaninello, A. (2016) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.1760 · Cited by 13 · see citing works
Cassani, G., Bianchi, F., & Marelli, M. (2021) · Cognitive Science · doi:10.1111/cogs.12963 · Cited by 14 · see citing works
Semantic shift in social networks
Noble, B., Sayeed, A., Fernández, R., & Larsson, S. (2021) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2021.starsem-1.3 · Cited by 13 · see citing works
Continual knowledge infusion into pre-trained biomedical language models
Jha, K., & Zhang, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab671 · Cited by 13 · see citing works
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 · see citing works
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 · see citing works
- 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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