References
- N. K. Nagwani, "A comment on "a similarity measure for text classification and clustering"," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 1, pp. 2589-2590, 2015. https://doi.org/10.1109/TKDE.2015.2451616
- A. Awajan, "Semantic similarity based approach for reducing Arabic texts dimensionality," International Journal of Speech Technology, vol. 19, no. 2, pp. 191-201, 2016. https://doi.org/10.1007/s10772-015-9284-6
- L. Xu, S. Sun and Q. Wang, "Text similarity algorithm based on semantic vector space model," in Proceedings of the 15th International Conference on Computer and Information Science, Okayama, Japan, 2016, pp. 1-4.
- R. lonescu and M. Popescu, Knowledge Transfer between Computer Vision and Text Mining: Similarity-Based Learning Approaches. Cham: Springer, 2016.
- E. Blanco and D. Moldovan, "A semantic logic-based approach to determine textual similarity," IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 23, no. 4, pp. 683-693, 2015. https://doi.org/10.1109/TASLP.2015.2403613
- M. Shirakawa, K. Nakayama, T. Hara, and S. Nishio, "Wikipedia-based semantic similarity measurements for noisy short texts using extended naive Bayes," IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 2, pp. 205-219, 2015. https://doi.org/10.1109/TETC.2015.2418716
- H. Z. Liu and P. F. Wang, "Accessing text semantic similarity using ontology," Journal of Software, vol. 9, no. 2, pp. 490-497, 2014.
- W. Song, C. H. Li, and S. C. Park, "Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures," Expert Systems with Applications, vol. 36, no. 5, pp. 9095-9104, 2009. https://doi.org/10.1016/j.eswa.2008.12.046
- Y. Wang and J. Hodges, "Document clustering with semantic analysis," in Proceedings of the 39th Annual Hawaii International Conference on System Sciences, Kauia, HI, 2006, pp. 54-63.
- R. M. Aliguliyev, "A new sentence similarity measure and sentence based extractive technique for automatic text summarization," Expert Systems with Applications, vol. 36, no. 4, pp. 7764-7772, 2009. https://doi.org/10.1016/j.eswa.2008.11.022
- L. Gang, C. Zheng and L. Zhang, "Text information retrieval based on concept semantic similarity," in Proceedings of the 5th International Conference on Semantics, Knowledge and Grid, Zhuhai, China, 2009, pp. 356-360.
- A. Hotho, S. Staab, and G. Stumme, "Ontologies improves text document clustering," in Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL, 2003, pp. 541-544.
- R. J. Bellegarda, "Exploiting latent semantic information in statistical language modeling," Proceedings of the IEEE, vol. 88, no. 8, pp. 1279-1296, 2000. https://doi.org/10.1109/5.880084
- C. Buck and P. Koehn, "Quick and reliable document alignment via TF/IDF-weighted cosine distance," in Proceedings of the 1st Conference on Machine Translation, Berlin, Germany, 2016, pp. 672-678.
- A. Mirzal, "Clustering and latent semantic indexing aspects of the singular value decomposition," International Journal of Information and Decision Sciences, vol. 8, no. 1, pp. 53-72, 2016. https://doi.org/10.1504/IJIDS.2016.075790
- G. Karypis, "CLUTO: a clustering toolkit," 2006 [Online]. Available: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview.