Ibrahim E. Fattoh

Work place: Computer Science Dept., Faculty of Information Technology, Misr University for Science & Technology, Egypt

E-mail: ibrahim.fattoh@must.edu.eg


Research Interests: Computer systems and computational processes, Artificial Intelligence, Natural Language Processing, Data Mining, Data Structures and Algorithms


Ibrahim E. Fattoh is currently working as a teacher assistant, Computer science department, Faculty of Information Technology, Misr University for Science and Technology (MUST), Cairo, Egypt. He is a Ph.D student, Faculty of Computer and Information, Helwan University, Egypt in the area of Automatic Question Generation under supervision of Prof. Mohammad Hassan Haggag and Assoc. Prof. Amal Elsayed Aboutabl. He finished his B. Sc. and Master degrees at Faculty of Computers and Information, Helwan University, Egypt. His master thesis was entitled "Supervised Immune System for Information Filtering". His research interests include Artificial Intelligence, Natural Language Processing, Data Mining, Text Mining, and Computational Intelligence.

Author Articles
Semantic Question Generation Using Artificial Immunity

By Ibrahim E. Fattoh Amal E. Aboutabl Mohamed H. Haggag

DOI: https://doi.org/10.5815/ijmecs.2015.01.01, Pub. Date: 8 Jan. 2015

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.

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