The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Text mining techniques for patent analysis
Overview
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Citations
Cites
Kohonen, T. (2001) · Springer Series in Information Sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,019
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,199
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
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
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 7,999
A Comparative Study on Feature Selection in Text Categorization
Yang, Y., & Pedersen, J. (1997) · Cited by 4,790
Automatic text processing: the transformation, analysis, and retrieval of information by computer
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0351 · Cited by 3,188
Advances in knowledge discovery and data mining
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90007-3 · Cited by 2,971
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
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
Cited by
A literature review on the state-of-the-art in patent analysis
Abbas, A., Zhang, L., & Khan, S. U. (2014) · World Patent Information · doi:10.1016/j.wpi.2013.12.006 · Cited by 477
Wagner, R., Preschitschek, N., Passerini, S., Leker, J., & Winter, M. (2013) · Journal of Applied Electrochemistry · doi:10.1007/s10800-013-0533-6 · Cited by 395
Explaining Data-Driven Document Classifications1
Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264
Extracting and mapping industry 4.0 technologies using wikipedia
Chiarello, F., Trivelli, L., Bonaccorsi, A., & Fantoni, G. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.04.006 · Cited by 230
Metadata
- Authors
- Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I.
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2007-09
- Volume
- 43
- Issue
- 5
- Pages
- 1216-1247
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W2062418945
- Cited by
- 782
- References
- 37
- FWCI
- 29.16
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Text mining techniques for patent analysis. https://app.cochat.ai/papers/10.1016/j.ipm.2006.11.011
- The paper
- Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing & Management. https://doi.org/10.1016/j.ipm.2006.11.011
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.
