State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems
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 chall...
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| Main Authors: | , |
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| Format: | Article |
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2022
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| Online Access: | http://scholars.utp.edu.my/id/eprint/33977/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139493366&doi=10.18576%2fisl%2f110540&partnerID=40&md5=06706c0295d8aea49907a94093c2581c |
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| Summary: | 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. |
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