TY  - JOUR
SP  - 1851
TI  - State-of-the-Art: Assessing Semantic Similarity in Automated Short-Answer Grading Systems
N1  - cited By 2
AV  - none
EP  - 1858
PB  - Natural Sciences Publishing
SN  - 20909551
ID  - scholars16376
N2  - 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 responderâ??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.
IS  - 5
VL  - 11
JF  - Information Sciences Letters
A1  - Amur, Z.H.
A1  - Hooi, Y.K.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139493366&doi=10.18576%2fisl%2f110540&partnerID=40&md5=06706c0295d8aea49907a94093c2581c
Y1  - 2022///
ER  -