Semantic Question Generation Using Artificial Immunity

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Ibrahim E. Fattoh 1,* Amal E. Aboutabl 2 Mohamed H. Haggag 2

1. Computer Science Dept., Faculty of Information Technology, Misr University for Science & Technology, Egypt

2. Computer Science Dept., Faculty of Computers & Information, Helwan University, Egypt

* Corresponding author.


Received: 2 Nov. 2014 / Revised: 5 Dec. 2014 / Accepted: 26 Dec. 2014 / Published: 8 Jan. 2015

Index Terms

Natural Language Processing, Automatic Question Generation, Semantic Role Labeling, Named Entity Recognition, Artificial Immune System


This research proposes an automatic question generation model for evaluating the understanding of semantic attributes in a sentence. The Semantic Role Labeling and Named Entity Recognition are used as a preprocessing step to convert the input sentence into a semantic pattern. The Artificial Immune System is used to build a classifier that will be able to classify the patterns according to the question type in the training phase. The question types considered here are the set of WH-questions like who, when, where, why, and how. A pattern matching phase is applied for selecting the best matching question pattern for the test sentence. The proposed model is tested against a set of sentences obtained from many sources such as the TREC 2007 dataset for question answering, Wikipedia articles, and English book of grade II preparatory. The experimental results of the proposed model are promising in determining the question type with classification accuracy reaching 95%, and 87% in generating the new question patterns.

Cite This Paper

Ibrahim E. Fattoh, Amal E. Aboutabl, Mohamed H. Haggag, "Semantic Question Generation Using Artificial Immunity", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.1, pp.1-8, 2015. DOI:10.5815/ijmecs.2015.01.01


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