A Systematic and Comparative Analysis of Semantic Search Algorithms
Priya Makarand Shelke, Chaitali Shewale, Riddhi Mirajkar, Suruchi Gaurav Dedgaonkar, Pawan Wawage, Riddhi Pawar · International Journal on Recent and Innovation Trends in Computing and Communication · 2023
Users often struggle to discover the information they need online because of the massive volume of data that is readily available as well as being generated every day in the today’s digital age. Traditional keyword-based search engines may not be able to handle complex queries, which could result in irrelevant or insufficient search results. This issue can be solved by semantic search, which utilises machine learning and natural language processing to interpret the meaning and context of a user's query. In this paper we focus on analyzing the BM-25 algorithm, Mean of Word Vectors approach, Universal Sentence Encoder model, and Sentence-BERT model on the CISI Dataset for Semantic Search Task. The results indicate that, the Finetuned SBERT model performs the best.