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Semantic Attributes Model for Automatic Generation of Multiple Choice Questions

by Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 1
Year of Publication: 2014
Authors: Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag
10.5120/18038-8544

Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag . Semantic Attributes Model for Automatic Generation of Multiple Choice Questions. International Journal of Computer Applications. 103, 1 ( October 2014), 18-24. DOI=10.5120/18038-8544

@article{ 10.5120/18038-8544,
author = { Ibrahim Eldesoky Fattoh, Amal Elsayed Aboutabl, Mohamed Hassan Haggag },
title = { Semantic Attributes Model for Automatic Generation of Multiple Choice Questions },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 1 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number1/18038-8544/ },
doi = { 10.5120/18038-8544 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:25.740015+05:30
%A Ibrahim Eldesoky Fattoh
%A Amal Elsayed Aboutabl
%A Mohamed Hassan Haggag
%T Semantic Attributes Model for Automatic Generation of Multiple Choice Questions
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 1
%P 18-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research, an automatic multiple choice question generation system for evaluating semantic role labels and named entities is proposed. The selection of the informative sentence and the keyword to be asked about are based on the semantic labels and named entities that exist in the question sentence. The research introduces a novel method for the distractor selection process. Distractors are chosen based on a string similarity measure between sentences in the data set. Eight algorithms of string similarity measures are used in this research. The system is tested using a set of sentences extracted from the data set for question answering. Experimental results prove that the semantic role labeling and named entity recognition approaches can be used for keyword selection. String similarity measures have been used in generating the distractors in the process of automatic multiple choice questions generation. Combining the similarity measures of some algorithms led to enhancing the results.

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Index Terms

Computer Science
Information Sciences

Keywords

Automatic Multiple Choice Question Generation String Similarity Measures Semantic Role Labeling Named Entity Recognition.

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