%L scholars16376
%X The use of semantic in Natural Language Processing (NLP) has sparked the interest of academics and businesses in various fields. One such field is Automated Short-answer Grading Systems (ASAGS) for automatically evaluating responses for similarity with the expected answer. ASAGS poses semantic challenges because the responses of a topic are in the responder�s own words. This study is providing an in-depth analysis of work to improve the assessment of semantic similarity between corpora in natural language in the context of ASAGS. Three popular semantic approaches are corpus-based, knowledge-based, and deep learning are used to evaluate against the conventional methods in ASAGS. Finally, the gaps in knowledge are identified and new research areas are proposed. © 2022 NSP.
%O cited By 2
%N 5
%D 2022
%A Z.H. Amur
%A Y.K. Hooi
%P 1851-1858
%I Natural Sciences Publishing
%J Information Sciences Letters
%T State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems
%R 10.18576/isl/110540
%V 11