Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora
Open accessAbstract
The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science.This is commonly approached by training word embeddings on each corpus, aligning the vector spaces, and looking for words whose cosine distance in the aligned space is large.However, these methods often require extensive filtering of the vocabulary to perform well, and-as we show in this work-result in unstable, and hence less reliable, results.We propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word.The method is simple, interpretable and stable.We demonstrate its effectiveness in 9 different setups, considering different corpus splitting criteria (age, gender and profession of tweet authors, time of tweet) and different languages (English, French and Hebrew).
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Some common correlation metrics were criticized: Kendall's tau could be misleading at the tail end of distributions and Spearman correlation has limitations for global rankings.
“We highlight that some common correlation metrics were criticized, such as Kendall's tau, which could be misleading at the tail end of distributions [165] and Spearman correlation has limitations for global rankings [82].”
From From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023) · cites this paper as [82]
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О слухах и их разоблачениях: как бороться с недостоверной информацией?
Петрова, Д. (2024) · Journal of Economic Sociology · doi:10.17323/1726-3247-2024-3-213-228 · Cited by 1 · see citing works
Measuring Alignment to Authoritarian State Media as Framing Bias
2020, T. 2. I. C. O. C. L., Kao, H.-Y., & Niven, T. (2020) · Underline Science Inc. · doi:10.48448/qp44-5g44 · Cited by 1 · see citing works
The Corpora They Are a-Changing: a Case Study in Italian Newspapers
Basile, P., Caputo, A., Caselli, T., Cassotti, P., & Varvara, R. (2021) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2021.lchange-1.3
Dore, G. M. D., McCarthy, A. D., & Scharf, J. A. (2023) · SpringerBriefs in political science · doi:10.1007/978-3-031-27584-5_4
Does Bert Know How ‘Virus’ Evolved: Tracking Usage Changes in Chinese Textual Data
Chen, J., Qiu, L., Peng, B., & Huang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0586-3_10
Харизма и повседневность в экономическом поле
Kalinina, V. (2024) · Journal of Economic Sociology · doi:10.17323/1726-3247-2024-3-160-182
Tikhomirova, A. (2024) · Journal of Economic Sociology · doi:10.17323/1726-3247-2024-3-247-257
- Authors
- Gonen, H., Jawahar, G., Seddah, D., & Goldberg, Y.
- Affiliations
- Bar-Ilan University (IL)
- Centre Inria de Paris (FR)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 538-555
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3035345764
- Cited by
- 59
- References
- 47
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 12 · 2023: 14 · 2022: 13 · 2021: 14 · 2020: 2
- FWCI
- 109.42
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- European Commission (HORIZON2020)
- Agence Nationale de la Recherche (ANR-16, ANR-16-CE33-0021, ANR-15-CE38-0011)
- Ministry of Science, Technology and Space
- H2020 European Research Council (802774)
- Topics
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- word usage change, corpus comparison, word embeddings, computational social science, digital humanities, multilingual text analysis
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.
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