Systematic Review of Semantic Analysis Methods

Abstract

NLP (natural language processing) is a very broad subcategory of computer science that focuses on human language processing in computers. Each different NLP processing technique focuses on different parts of linguistics, with semantics being the main focus of this manuscript. This manuscript aims to utilize a systematic review of several published papers ranging from 1998 to the current day to review and summarize critical analysis regarding the three main models that this paper focuses on: Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Neural Network based models. This manuscript compares these models against each other, weighs their advantages and disadvantages, and provides their uses. The selected studies in this review were analyzed and examined to ensure that they meet the quality and standards of the proposed research methodology. The results show that Neural network-based solutions are the most popular Semantic Analysis model in Academia (doubling the number of results of ESA and LSA combined), and they are usually the best in most tasks. However, there are specific scenarios and circumstances in which relying on the older LSA and ESA models could be beneficial.