A Comparative Analysis of TF-IDF, LSI and LDA in Semantic Information Retrieval Approach for Paper-Reviewer Assignment

Abstract

The intelligent task of semantically assigning a paper to a reviewer with respect to his knowledge domain remains a challenging task in academic conferences. From literature, a number of automated reviewer assignment systems have been presented which are based on distributional semantic models such as Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) have been used to capture semantics. Thus, this study presents the comparative study of the three models based on their derived suitability scores between a paper meant for review and a reviewer’s representation papers. From the experimental results obtained, it shows that TF-IDF outperformed the accuracy level of the other two models by a substantial margin.