@article{scholars16376,
       publisher = {Natural Sciences Publishing},
            year = {2022},
         journal = {Information Sciences Letters},
            note = {cited By 2},
           pages = {1851--1858},
             doi = {10.18576/isl/110540},
           title = {State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems},
          volume = {11},
          number = {5},
            issn = {20909551},
          author = {Amur, Z. H. and Hooi, Y. K.},
        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{\^a}??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. {\^A}{\copyright} 2022 NSP.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139493366&doi=10.18576\%2fisl\%2f110540&partnerID=40&md5=06706c0295d8aea49907a94093c2581c}
}