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