AlQuAnS – An Arabic Language Question Answering System

Mohamed Nabil, Ahmed Abdelmegied, Yasmin Ayman, Ahmed Fathy, Ghada Khairy, Mohammed Yousri, Nagwa El-Makky, Khaled Nagi

Abstract

Building Arabic Question Answering systems is a challenging problem compared to their English counter-parts due to several limitations inherent in the Arabic language and the scarceness of available Arabic training datasets. In our proposed Arabic Question Answering system, we combine several previously successful algorithms and add a novel approach to the answer extraction process that has not been used by any Arabic Question Answering system before. We use the state-of-the-art MADAMIRA Arabic morphological analyser for preprocessing questions and retrieved passages. We also enhance and extend the question classification and use the Explicit Semantic Approach (ESA) in the passage retrieval process to rank passages that most probably contain the correct answer. We also introduce a new answer extraction pattern, which matches the patterns formed according to the question type with the sentences in the retrieved passages in order to provide the correct answer. A performance evaluation study shows that our system gives promising results compared to other existing Arabic Question Answering systems, especially with the newly introduced answer extraction module.

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Paper Citation


in Harvard Style

Nabil M., Abdelmegied A., Ayman Y., Fathy A., Khairy G., Yousri M., El-Makky N. and Nagi K. (2017). AlQuAnS – An Arabic Language Question Answering System.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 144-154. DOI: 10.5220/0006602901440154


in Bibtex Style

@conference{kdir17,
author={Mohamed Nabil and Ahmed Abdelmegied and Yasmin Ayman and Ahmed Fathy and Ghada Khairy and Mohammed Yousri and Nagwa El-Makky and Khaled Nagi},
title={AlQuAnS – An Arabic Language Question Answering System},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={144-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006602901440154},
isbn={978-989-758-271-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - AlQuAnS – An Arabic Language Question Answering System
SN - 978-989-758-271-4
AU - Nabil M.
AU - Abdelmegied A.
AU - Ayman Y.
AU - Fathy A.
AU - Khairy G.
AU - Yousri M.
AU - El-Makky N.
AU - Nagi K.
PY - 2017
SP - 144
EP - 154
DO - 10.5220/0006602901440154