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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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(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Quantitative Analysis of Culture Using Millions of Digitized Books
Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., et al. (2010) · Science · doi:10.1126/science.1199644 · Cited by 3,235
Linguistic Regularities in Continuous Space Word Representations
Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,913
Mapping authors in intellectual space: A technical overview
McCain, K. W. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6 · Cited by 987
Quantifying the evolutionary dynamics of language
Aiden, E. L., Michel, J.-B., Jackson, J., Tang, T., & Nowak, M. A. (2007) · Nature · doi:10.1038/nature06137 · Cited by 531
Syntactic Annotations for the Google Books NGram Corpus
Lin, Y., Michel, J., Aiden, E. L., Orwant, J., Brockman, W., & Petrov, S. (2012) · Cited by 423
Self-citationLearning Discriminative Projections for Text Similarity Measures
Yih, W.-T., Toutanova, K., Platt, J. C., & Meek, C. (2011) · Cited by 214
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Agmon, S., Singer, U., & Radinsky, K. (2024) · JMIR AI · doi:10.2196/49546 · Cited by 3
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Sense Tree: Discovery of New Word Senses with Graph-based Scoring.
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Emms, M., & Jayapal, A. (2016) · International Conference on Computational Linguistics · Cited by 3
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Giorcelli, M., Lacetera, N., & Marinoni, A. (2019) · National Bureau of Economic Research · doi:10.3386/w25429 · Cited by 3
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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