The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(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
Cited by
Sorted by citations
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1141 · Cited by 1,170
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585
Statistically Significant Detection of Linguistic Change
Kulkarni, V., Al-Rfou, R., Perozzi, B., & Skiena, S. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741627 · Cited by 390
Distributional Semantics and Linguistic Theory
Boleda, G. (2019) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-011619-030303 · Cited by 328
Siamese CBOW: Optimizing Word Embeddings for Sentence Representations
Kenter, T., Borisov, A., & de Rijke, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1089 · Cited by 287
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 ›
Are you an author, or do you see a problem with this page? Request a review
