The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Probabilistic author-topic models for information discovery
Abstract
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.
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.
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
Indexing by latent semantic analysis
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9 · Cited by 12,751
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Self-citationProbabilistic Latent Semantic Indexing
Hofmann, T. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130370 · Cited by 3,988
Probabilistic latent semantic indexing
Hofmann, T. (1999) · Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/312624.312649 · Cited by 3,976
Scientific collaboration networks. I. Network construction and fundamental results
Newman, M. E. J. (2001) · Physical Review E · doi:10.1103/physreve.64.016131 · Cited by 2,076
An Evaluation of Statistical Approaches to Text Categorization
Yang, Y. (1999) · Information Retrieval · doi:10.1023/a:1009982220290 · Cited by 1,943
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections
Cutting, D. R., Karger, D. R., Pedersen, J. O., & Tukey, J. W. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130362 · Cited by 1,419
The author-topic model for authors and documents
Rosen‐Zvi, M., Griffiths, T. L., Steyvers, M., & Smyth, P. (2004) · Uncertainty in Artificial Intelligence · doi:10.5555/1036843.1036902 · Cited by 1,280
Self-citationEfficient clustering of high-dimensional data sets with application to reference matching
McCallum, A., Nigam, K., & Ungar, L. H. (2000) · Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/347090.347123 · Cited by 1,096
Cited by
ArnetMiner: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1402008 · Cited by 2,231
Comparing Twitter and Traditional Media Using Topic Models
Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20161-5_34 · Cited by 1,214
Topics in semantic representation
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.211 · Cited by 1,122
Self-citationIdentifying miRNAs, targets and functions
Liu, B., Li, J., & Cairns, M. J. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbs075 · Cited by 563
Ranking-based clustering of heterogeneous information networks with star network schema
Sun, Y., Yu, Y., & Han, J. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557107 · Cited by 513
Metadata
- Authors
- Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T.
- Venue
- Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2004-08-22
- Pages
- 306-315
- OpenAlex
- W2109154616
- Cited by
- 572
- References
- 26
- FWCI
- 8.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Probabilistic author-topic models for information discovery. https://app.cochat.ai/papers/10.1145/1014052.1014087
- The paper
- Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (2004). Probabilistic author-topic models for information discovery. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1014052.1014087
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
