Exploring the Effects of Root Expansion, Sentence Splitting and Ontology on Arabic Answer Selection
Ahmed Magdy Ezzeldin, Yasser El-Sonbaty, Mohamed Kholief · Natural Language Processing and Cognitive Science · 2015
Question answering systems generally, and Arabic systems are no exception, hit an upper bound of performance due to the propagation of error in their pipeline. This increases the significance of answer selection systems as they enhance the certainty and accuracy of question answering. Very few works tackled the Arabic answer selection problem, and they did not demonstrate encouraging performance because they use the same question answering pipeline without any changes to satisfy the requirements of answer selection. In this paper, we present “ALQASIM 2.0”, which uses a new approach to Arabic answer selection. It analyzes the reading test documents instead of the questions, utilizes sentence splitting, root expansion, and semantic expansion using an automatically generated ontology. Our experiments are conducted on the test-set provided by CLEF 2012 through the task of QA4MRE. This approach leads to a promising performance of 0.36 accuracy and 0.42 c@1.