The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning approaches for detecting and tracking news events
Open accessAbstract
The authors extend existing supervised-learning and unsupervised-clustering algorithms to allow document classification based on the information content and temporal aspects of news events. They've adapted several IR and machine learning techniques for effective event detection and tracking. The article discusses our research using manually segmented documents.
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
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Cited by
Efficient phrase-based document indexing for Web document clustering
Hammouda, K. M., & Kamel, M. S. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.58 · Cited by 316
Metadata
- Authors
- Yang, Y., Carbonell, J. G., Brown, R. D., Pierce, T., Archibald, B. T., & Liu, X.
- Venue
- IEEE Intelligent Systems
- Published
- 1999-07
- Volume
- 14
- Issue
- 4
- Pages
- 32-43
- ISSN
- 1094-7167 (ISSN-L)
- OpenAlex
- W2079234336
- Cited by
- 339
- References
- 1
- FWCI
- 15.06
- Type
- journal article
- Language
- en
- Open access
- green
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: Learning approaches for detecting and tracking news events. https://app.cochat.ai/papers/10.1109/5254.784083
- The paper
- Yang, Y., Carbonell, J. G., Brown, R. D., Pierce, T., Archibald, B. T., & Liu, X. (1999). Learning approaches for detecting and tracking news events. IEEE Intelligent Systems. https://doi.org/10.1109/5254.784083
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.
