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A novel approach for dimension reduction using word embedding: An enhanced text classification approach
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“The utilization of LSA substantially enhances our capacity to extract and discern the semantic associations that manifest in the thoughts of speakers or writers during communication.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Singh et al. 2022
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Cites
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
Research works printed but not linked: 25
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- Andrews, M. (2016). Compressing Word Embeddings. The 23rd International Conference on Neural Information Processing (ICONIP), Japan. http://arxiv.org/abs/1511.06397 · DOI
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- Carreira-Perpinan, M. A. (.1997). A review of dimension reduction techniques. technical report cs 96-09, Dept. of Computer Science, University of Sheffield.
- Chunxiu, 2007, Research of Word Similarity Computation, Information studies: Theory & Application, 6, 22
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- Dumais, 2005, Latent Semantic Analysis, Annual Review of Information Science and Technology, 38, 188, 10.1002/aris.1440380105 · DOI
- Dzisevic, 2019, Text Classification using Different Feature Extraction Approaches, Open Conference of Electrical, Electronic and Information Sciences (eStream), 10.1109/eStream.2019.8732167 · DOI
- Hoque, 2014, MIFS-ND: A mutual information-based feature selection method, Expert Systems with Applications, 10.1016/j.eswa.2014.04.019 · DOI · Paper page
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- Karl, 1901, On lines and planes of closest fit to systems of points in space, Philosophical Magazine Series, 2, 559, 10.1080/14786440109462720 · DOI
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- Kim, 2005, Dimension Reduction in Text Classification with Support Vector Machines, Journal of Machine Learning Research, 6, 37
- Kushwaha, 2021, Applications of big data in emerging management disciplines: A literature review using text mining, International Journal of Information Management Data Insights, 1, 10.1016/j.jjimei.2021.100017 · DOI · Paper page
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- Ling, S., Song, Y., & Roth, D. (2016). Word embeddings with limited memory. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Germany, 387–392. https://doi.org/10.18653/v1/P16-2063 · DOI
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Cited by
Sorted by citations
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Teng, T. H., & Varathan, K. D. (2023) · IEEE Access · doi:10.1109/access.2023.3275130 · Cited by 77
Metadata
- Authors
- Singh, K. N., Devi, S. D., Devi, H. M., & Mahanta, A. K.
- Venue
- International Journal of Information Management Data Insights
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 2
- Issue
- 1
- Pages
- 100061
- ISSN
- 2667-0968
- Cited by
- 75
- References
- 4
- Language
- en
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