Abstract
In this paper, we present a hybrid model for answer selection in question answering systems by representing multiple kinds of features, i.e., lexical-based, word-alignment, and word-embedding. The model employs convolutional neural network, multilayer perceptron, and support vector machines to train the classifiers. We evaluate our model on the two popular QA datasets, SemEval-2016 Task 3 and TREC QA. The experimental results show that our system outperforms the top-5 proposed systems in SemEval-2016 workshop, and also achieves the-state-of-art results on TREC QA dataset.
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Duong, P.H., Nguyen, H.T., Nguyen, D.D., Do, H.T. (2018). A Hybrid Approach to Answer Selection in Question Answering Systems. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_16
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