@article{3223,
  author = {Nemanja Popovic, Suzana Stojkovic},
  title = {Analysis of Classification Algorithms for using in Vertical Retrieval Systems},
  journal = {International Journal of Computational Linguistics Research},
  year = {2021},
  volume = {12},
  number = {1},
  doi = {https://doi.org/10.6025/jcl/2021/12/1/1-8},
  url = {https://www.dline.info/jcl/fulltext/v12n1/jclv12n1_1.pdf},
  abstract = {Classification is the most solved and the most used machine learning problem. In a last few decades many classification algorithms have been developed. Because of that, when classification is needed in some problem solving, the best algorithm should always be selected. The problem that is analysed in this paper is choosing classification algorithms that can be used in vertical retrieval system for both document and query classification. We compared SVM, Multinomial
Naïve Bayes algorithm, Bernoulli Naïve Bayes algorithm and Random forest. The experiments presented in the paper, show that in the long documents classification SVM and Multinomial Naïve Bayes algorithms have a similar precision (SVM is a little better), but the Multinomial Naive Bayes algorithm correctly classified 93.14% of queries, while SVM only 22.55%.},
}