TY  - JOUR
T1  - A Comparative Analysis of TF-IDF, LSI and LDA in Semantic Information
Retrieval Approach for Paper-Reviewer Assignment
AU - Adebiyi, A. Ayodele AU - Ogunleye, Olawole AU - Adebiyi, O. Marion AU - Okesola, J. Olatunji 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 10
SP  - 3378
EP  - 3382
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.3378.3382
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.3378.3382
KW  - Reviewer-assignment
KW  -latent semantic indexing
KW  -latent dirichlet allocation
KW  -term frequency-inverse
document frequency
KW  -semantically
KW  -academic conferences
AB  - 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.
ER  -