The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
The use of bigrams to enhance text categorization
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
Introduction to Modern Information Retrieval
Salton, G., & McGill, M. J. (1983) · Virtual Defense Library (Ministerio de Defensa) · Cited by 9,680
Introduction to modern information retrieval
Dillon, M. (1983) · Information Processing & Management · doi:10.1016/0306-4573(83)90062-6 · Cited by 5,909
A Comparative Study on Feature Selection in Text Categorization
Yang, Y., & Pedersen, J. (1997) · Cited by 4,790
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840
A re-examination of text categorization methods
Yang, Y., & Liu, X. (1999) · Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/312624.312647 · Cited by 2,652
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026666 · Cited by 2,104
Automated learning of decision rules for text categorization
Apté, C., Damerau, F., & Weiss, S. M. (1994) · ACM Transactions on Information Systems · doi:10.1145/183422.183423 · Cited by 859
Feature selection and feature extraction for text categorization
Lewis, D. D. (1992) · Proceedings of the workshop on Speech and Natural Language - HLT '91 · doi:10.3115/1075527.1075574 · Cited by 578
An evaluation of phrasal and clustered representations on a text categorization task
Lewis, D. D. (1992) · Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '92 · doi:10.1145/133160.133172 · Cited by 544
A comparison of classifiers and document representations for the routing problem
Schütze, H., Hull, D. A., & Pedersen, J. O. (1995) · Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95 · doi:10.1145/215206.215365 · Cited by 453
Cited by
The text mining handbook: advanced approaches in analyzing unstructured data
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5684 · Cited by 1,598
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Johnson, R., & Zhang, T. (2015) · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.3115/v1/n15-1011 · Cited by 864
Corporate Climate Risk: Measurements and Responses
Li, Q., Shan, H., Tang, Y., & Yao, V. (2024) · The Review of Financial Studies · doi:10.1093/rfs/hhad094 · Cited by 560
Big Data: Deep Learning for financial sentiment analysis
Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018) · Journal of Big Data · doi:10.1186/s40537-017-0111-6 · Cited by 390
Automatic text classification to support systematic reviews in medicine
García Adeva, J. J., Pikatza Atxa, J. M., Ubeda Carrillo, M., & Ansuategi Zengotitabengoa, E. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.047 · Cited by 125
Metadata
- Authors
- Tan, C.-M., Wang, Y.-F., & Lee, C.-D.
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2002-07
- Volume
- 38
- Issue
- 4
- Pages
- 529-546
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W2027875450
- Cited by
- 290
- References
- 15
- FWCI
- 11.71
- Type
- journal article
- 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: The use of bigrams to enhance text categorization. https://app.cochat.ai/papers/10.1016/s0306-4573(01)00045-0
- The paper
- Tan, C.-M., Wang, Y.-F., & Lee, C.-D. (2002). The use of bigrams to enhance text categorization. Information Processing & Management. https://doi.org/10.1016/s0306-4573(01)00045-0
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.
