eprintid: 16376 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/63/76 datestamp: 2023-12-19 03:22:54 lastmod: 2023-12-19 03:22:54 status_changed: 2023-12-19 03:06:08 type: article metadata_visibility: show creators_name: Amur, Z.H. creators_name: Hooi, Y.K. title: State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems ispublished: pub note: cited By 2 abstract: 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. date: 2022 publisher: Natural Sciences Publishing official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139493366&doi=10.18576%2fisl%2f110540&partnerID=40&md5=06706c0295d8aea49907a94093c2581c id_number: 10.18576/isl/110540 full_text_status: none publication: Information Sciences Letters volume: 11 number: 5 pagerange: 1851-1858 refereed: TRUE issn: 20909551 citation: Amur, Z.H. and Hooi, Y.K. (2022) State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems. Information Sciences Letters, 11 (5). pp. 1851-1858. ISSN 20909551