Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 676

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

A comparative study of TF*IDF, LSI and multi-words for text classification

Zhang, Wen, Yoshida, Taketoshi, Tang, Xijin·2011·Expert Systems with Applications

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.eswa.2010.08.066

Cites

38 of 38 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 38 references

Research works printed but not linked: 10

  1. Chen, J. Yeh, C., & Chau, R. (2006). Identifying multi-word terms by text-segments. In Proceedings of the seventh international conference on web-age information management workshops (pp. 10–19). Hongkong. · doi:10.1109/waimw.2006.16
  2. Ho, 1998, Information retrieval using rough sets, Journal of the Japanese Society for Artificial Intelligence, 13, 424
  3. Ho, 2000, Non-hierarchical document clustering based on a tolerance rough set model, International Journal of intelligent systems, 17, 199, 10.1002/int.10016 · doi:10.1002/int.10016
  4. Kolda, 1998, A semidiscrete matrix decomposition for latent semantic indexing in information retrieval, ACM Transactions on Information Systems, 16, 322, 10.1145/291128.291131 · doi:10.1145/291128.291131
  5. Li, 2008, Text document clustering based on frequent word meaning sequences, Data & Knowledge Engineering, 64, 381, 10.1016/j.datak.2007.08.001 · doi:10.1016/j.datak.2007.08.001
  6. Papka, R., & Allan, J. (1998). Document classification using multiword features. Document classification using multi-word features. In Proceedings of the seventh international conference on information and knowledge management (pp. 124–131). Bethesda, Maryland, United States. · doi:10.1145/288627.288648
  7. Yan, 2008, Augmenting the power of LSI in text retrieval: Singular value rescaling, Data & Knowledge Engineering, 65, 108, 10.1016/j.datak.2007.10.005 · doi:10.1016/j.datak.2007.10.005
  8. Zhang, W., Yoshida, T., & Tang, X. J. (2007). Text classification with multi-words. In Proceedings of 2007 IEEE international conference on system, man, and cybernetics (pp. 3519–3524). Montreal Canada.
  9. Zhang, 2009, Using ontology to improve precision of terminology extraction from documents, Expert Systems with Applications, 36, 9333, 10.1016/j.eswa.2008.12.034 · doi:10.1016/j.eswa.2008.12.034
  10. Zhang, 2009, Augmented mutual Information for multi-word extraction, International Journal of Innovative Computing, Information and Control, 5, 543

Cited by

See all 676

Metadata

Authors
Zhang, W., Yoshida, T., & Tang, X.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2011-03
Volume
38
Issue
3
Pages
2758-2765
ISSN
0957-4174 (ISSN-L)
OpenAlex
W2150874198
Cited by
676
References
38
FWCI
10.20
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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: A comparative study of TF*IDF, LSI and multi-words for text classification. https://app.cochat.ai/papers/10.1016/j.eswa.2010.08.066
The paper
Zhang, W., Yoshida, T., & Tang, X. (2011). A comparative study of TF*IDF, LSI and multi-words for text classification. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2010.08.066

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.