The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Content-based hierarchical document organization using multi-layer hybrid network and tree-structured features
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Tree-structured features representation is a rich data characterization, and ML-SOM are very effective in handling such contextual information [134], [142].”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [142]
Citations
Cites
Neural Networks: A Comprehensive Foundation
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Term-weighting approaches in automatic text retrieval
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Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,248
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
Using Linear Algebra for Intelligent Information Retrieval
Berry, M. W., Dumais, S. T., & O’Brien, G. W. (1995) · SIAM Review · doi:10.1137/1037127 · Cited by 1,481
Papadimitriou, C. H., Tamaki, H., Raghavan, P., & Vempala, S. (1998) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/275487.275505 · Cited by 862
Hierarchically Classifying Documents Using Very Few Words
Koller, D., & Sahami, M. (1997) · Cited by 854
Hierarchical classification of Web content
Dumais, S., & Chen, H. (2000) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/345508.345593 · Cited by 810
Salembier, P., & Garrido, L. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.841934 · Cited by 587
Latent Semantic Indexing: A Probabilistic Analysis
Papadimitriou, C. H., Raghavan, P., Tamaki, H., & Vempala, S. (2000) · Journal of Computer and System Sciences · doi:10.1006/jcss.2000.1711 · Cited by 577
Cited by
Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299
An improved plagiarism detection scheme based on semantic role labeling
Osman, A. H., Salim, N. B., Binwahlan, M. S., Alteeb, R., & Abuobieda, A. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2011.12.021 · Cited by 98
Dimoulas, C. A., Papanikolaou, G. V., & Petridis, V. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.115 · Cited by 52
Survey of Text Plagiarism Detection
Osman, A. H., Salim, N. B., & Abuobieda, A. (2012) · Computer Engineering and Applications Journal · doi:10.18495/comengapp.v1i1.5 · Cited by 37
Feature unionization: A novel approach for dimension reduction
Jalilvand, A., & Salim, N. B. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.08.031 · Cited by 30
Document Plagiarism Detection Using a New Concept Similarity in Formal Concept Analysis
Muangprathub, J., Kajornkasirat, S., & Wanichsombat, A. (2021) · Journal of Applied Mathematics · doi:10.1155/2021/6662984 · Cited by 25
A New Hybrid Technique for Detection of Plagiarism from Text Documents
Ahuja, L., Gupta, V., & Kumar, R. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04565-9 · Cited by 29
GINN: gradient interpretable neural networks for visualizing financial texts
Ito, T., Sakaji, H., Izumi, K., Tsubouchi, K., & Yamashita, T. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0160-8 · Cited by 20
Similarity measure models and algorithms for hierarchical cases
Wu, D., Jie Lu, B., & Zhang, G. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.05.040 · Cited by 18
Fan, Y., Liu, C., & Wang, J. (2018) · Advanced Engineering Informatics · doi:10.1016/j.aei.2018.04.012 · Cited by 12
(2013) · Multimedia Information Retrieval · doi:10.1016/b978-1-84334-722-4.50019-9 · Cited by 1
Metadata
- Authors
- Rahman, M. K. M., & Chow, T. W. S.
- Affiliations
- City University of Hong Kong (HK)
- Corresponding author
- Chow, Tommy W.S.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 37
- Issue
- 4
- Pages
- 2874-2881
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2150838259
- Cited by
- 11
- References
- 30
- Citations by year
- 2021: 1 · 2020: 1 · 2018: 2 · 2016: 1 · 2013: 1 · 2012: 2
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- City University of Hong Kong
- University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- hybrid neural network, self-organizing maps, multilayer perceptron, document structure, document semantics
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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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.
