The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Studying the history of ideas using topic models
Overview
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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Claims other papers make about this one
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The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We propose that the increased use of shared evaluations, and the statistical methodology borrowed originally from electrical engineering (Hall et al., 2008;Anderson et al., 2012) has led NLP to undergo a shift towards rapid discovery science.”
From Measuring the Evolution of a Scientific Field through Citation Frames (2018) · cites this paper as Hall et al., 2008
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Citations
Cites
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Research works printed but not linked: 11
- Steven Bird. 2008. Association of Computational Lin- guists Anthology. http://www.aclweb.org/anthology- index/.
- David Blei and John D. Lafferty. 2006. Dynamic topic models. ICML.
- David Blei, Andrew Ng, , and Michael Jordan. 2003. La- tent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022.
- Kenneth Church. 2005. Reviewing the reviewers. Com- put. Linguist., 31(4):575-578.
- Laura Dietz, Steffen Bickel, and Tobias Scheffer. 2007. Unsupervised prediction of citation influences. In ICML, pages 233-240. ACM.
- Yookyung Jo, Carl Lagoze, and C. Lee Giles. 2007. Detecting research topics via the correlation between graphs and texts. In KDD, pages 370-379, New York, NY, USA. ACM.
- Mark T. Joseph and Dragomir R. Radev. 2007. Citation analysis, centrality, and the ACL anthology. Techni- cal Report CSE-TR-535-07, University of Michigan. Department of Electrical Engineering and Computer Science.
- Thomas S. Kuhn. 1962. The Structure of Scientific Rev- olutions. University Of Chicago Press.
- Wei Li and Andrew McCallum. 2006. Pachinko alloca- tion: DAG-structured mixture models of topic correla- tions. In ICML, pages 577-584, New York, NY, USA. ACM.
- Gideon S. Mann, David Mimno, and Andrew McCal- lum. 2006. Bibliometric impact measures leveraging topic analysis. In JCDL '06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, pages 65-74, New York, NY, USA. ACM.
- Xuerui Wang and Andrew McCallum. 2006. Topics over time: a non-Markov continuous-time model of topical trends. In KDD, pages 424-433, New York, NY, USA. ACM.
Cited by
Sorted by citations
topicmodels : An R Package for Fitting Topic Models
Grün, B., & Hornik, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i13 · Cited by 1,072
A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk
Hara, K., Adams, A., Milland, K., Savage, S., Callison-Burch, C., & Bigham, J. P. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174023 · Cited by 497
Delving into LLM-assisted writing in biomedical publications through excess vocabulary
Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2024) · arXiv · doi:10.48550/arxiv.2406.07016 · Cited by 214
Measuring the Evolution of a Scientific Field through Citation Frames
Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00028 · Cited by 212
Self-citationDelving into LLM-assisted writing in biomedical publications through excess vocabulary
Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2025) · Science Advances · doi:10.1126/sciadv.adt3813 · Cited by 213
“The rise and fall of scientific disciplines is traceable in scholarly writing (Hall et al., 2008; Bizzoni et al., 2020).”
The utility of topic modelling for discourse studies: a critical evaluation
Brookes, G., & McEnery, T. (2019) · Discourse Studies · doi:10.1177/1461445618814032 · Cited by 175
Predicting the impact of scientific concepts using full-text features
McKeown, K., Daume, H., Chaturvedi, S., Paparrizos, J., Thadani, K., Barrio, P., Biran, O., Bothe, S., Collins, M., Fleischmann, K. R., et al. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23612 · Cited by 73
Metadata
- Authors
- Hall, D., Jurafsky, D., & Manning, C. D.
- Venue
- Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
- Published
- 2008
- Pages
- 363
- Cited by
- 191
- References
- 1
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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