Detecting semantic errors in a text is still a challenging area of investigation. A lot of research has been done on lexical and syntactic errors while fewer studies have tackled semantic errors, as they are more difficult to treat. The objective of this survey is to examine the existing studies, highlighting the current issues and suggesting the potential directions of future research. This survey is a result of analyzing different research papers and studying different approaches used for EDC. We also present a possible scheme for the classification of Semantic errors. This survey article also traces the evolution of different methods, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network-based methods, and hybrid methods. Discussing the strengths and weaknesses of each method. Among the main observations, we found that not many studies have been done in the field of semantic errors and also there is lack of proper datasets for semantic errors which can help in training and testing of the machine learning models. We present a table that summarizes these approaches along different dimensions such as target error types, linguistic dataset used, strengths and focus of each approach. This facilitates better understandability, comparison and evaluation of previous research. This survey provides a comprehensive view of existing systems in place, for new researchers to experiment and develop innovative ideas to address the issue of semantic error correction and detection.

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