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State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems |
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PP: 1851-1858 |
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doi:10.18576/isl/110540
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Author(s) |
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Zaira Hassan Amur,
Yew Kwang Hooi,
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Abstract |
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| 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.
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