Short-answer grading using textual entailment

Rohini Basak; Sudip Kumar Naskar; Alexander Gelbukh

doi:10.3233/JIFS-179038

Short-answer grading using textual entailment

Rohini Basak, Sudip Kumar Naskar, Alexander Gelbukh

Centro de Investigación en Computación (CIC)

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Given a question, a reference answer, and the answer given by the student, the aim of the automatic short answer grading task is to assign a grade to the students answer.We use for this a large number of matching rules relying on recognizing entailment relation between dependency structures of the two answers. Comparison of the grades generated by our method with those given by human judges on a computer science dataset shows a quite promising maximum correlation of 0.627.

Original language	English
Pages (from-to)	4909-4919
Number of pages	11
Journal	Journal of Intelligent and Fuzzy Systems
Volume	36
Issue number	5
DOIs	https://doi.org/10.3233/JIFS-179038
State	Published - 2019

Keywords

Automatic short answer grading
Dependency parsing
Recognizing textual entailment
Semantic similarity

Access to Document

10.3233/JIFS-179038

Cite this

@article{fe235a554182453f8ea091fb36e68e3e,

title = "Short-answer grading using textual entailment",

abstract = "Given a question, a reference answer, and the answer given by the student, the aim of the automatic short answer grading task is to assign a grade to the students answer.We use for this a large number of matching rules relying on recognizing entailment relation between dependency structures of the two answers. Comparison of the grades generated by our method with those given by human judges on a computer science dataset shows a quite promising maximum correlation of 0.627.",

keywords = "Automatic short answer grading, Dependency parsing, Recognizing textual entailment, Semantic similarity",

author = "Rohini Basak and Naskar, {Sudip Kumar} and Alexander Gelbukh",

note = "Publisher Copyright: {\textcopyright} 2019 - IOS Press and the authors.",

year = "2019",

doi = "10.3233/JIFS-179038",

language = "Ingl{\'e}s",

volume = "36",

pages = "4909--4919",

journal = "Journal of Intelligent and Fuzzy Systems",

issn = "1064-1246",

number = "5",

}

TY - JOUR

T1 - Short-answer grading using textual entailment

AU - Basak, Rohini

AU - Naskar, Sudip Kumar

AU - Gelbukh, Alexander

PY - 2019

Y1 - 2019

N2 - Given a question, a reference answer, and the answer given by the student, the aim of the automatic short answer grading task is to assign a grade to the students answer.We use for this a large number of matching rules relying on recognizing entailment relation between dependency structures of the two answers. Comparison of the grades generated by our method with those given by human judges on a computer science dataset shows a quite promising maximum correlation of 0.627.

AB - Given a question, a reference answer, and the answer given by the student, the aim of the automatic short answer grading task is to assign a grade to the students answer.We use for this a large number of matching rules relying on recognizing entailment relation between dependency structures of the two answers. Comparison of the grades generated by our method with those given by human judges on a computer science dataset shows a quite promising maximum correlation of 0.627.

KW - Automatic short answer grading

KW - Dependency parsing

KW - Recognizing textual entailment

KW - Semantic similarity

UR - http://www.scopus.com/inward/record.url?scp=85066427103&partnerID=8YFLogxK

U2 - 10.3233/JIFS-179038

DO - 10.3233/JIFS-179038

M3 - Artículo

AN - SCOPUS:85066427103

SN - 1064-1246

VL - 36

SP - 4909

EP - 4919

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

IS - 5

ER -

Short-answer grading using textual entailment

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this