Abstract
A Question Classification is an important task in Question Answering Systems and Information Retrieval among other NLP systems. Given a question, the aim of Question Classification is to find the correct type of answer for it. The focus of this paper is on Arabic question classification. We present a novel approach that combines a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN). This method works in two stages: in the first stage, we identify the coarse/main question class using an SVM model; in the second stage, for each coarse question class returned by the SVM model, a CNN model is used to predict the subclass (finer class) of the main class. The performed tests have shown that our approach to Arabic Questions Classification yields very promising results.
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Notes
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A closer scrutiny of the patterns that were used in [2] has shown that they do not cover all the possible variations of uses of Interrogative Patterns in different contexts and settings.
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The dataset is available at http://cogcomp.org/Data/QA/QC/.
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Aouichat, A., Hadj Ameur, M.S., Geussoum, A. (2018). Arabic Question Classification Using Support Vector Machines and Convolutional Neural Networks. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_12
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