Comparison of Latent Semantic Analysis and Vector Space Model for Automatic Identification of Competent Reviewers to Evaluate Papers

The assignment of reviewers to papers is one of the most important and challenging tasks in organizing scientific events. A major part of it is the correct identification of proper reviewers. This article presents a series of experiments aiming to test whether the latent semantic analysis (LSA) could be reliably used to identify competent reviewers to evaluate submitted papers. It also compares the performance of the LSA, the vector space model (VSM) and the method of explicit document description by a taxonomy of keywords, in computing accurate similarity factors between papers and reviewers. All the three methods share the same input datasets, taken from real-life conferences and the produced paper-reviewer similarities are evaluated with the same evaluation methods, allowing a fair and objective comparison between them. Experimental results show that in most cases LSA outperforms VSM and could even slightly outperform the explicit document description by a taxonomy of keywords, if the term-document matrix is composed of TF-IDF values, rather than the raw number of term occurrences.

@article{Kalmukov2022,
  title     = {Comparison of Latent Semantic Analysis and Vector Space Model for Automatic Identification of Competent Reviewers to Evaluate Papers},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Yordan Kalmukov},
  doi       = {10.14569/IJACSA.2022.0130209},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130209}
}