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WIREs Data Mining Knowl Discov
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Text‐based question answering from information retrieval and deep neural network perspectives: A survey

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Abstract Text‐based question answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval (IR) techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning (DL) approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between the questions and the answer sentences. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both a traditional IR perspective and a more recent deep neural network environment. We also introduce well‐known datasets for the task and present available results from the literature to have a comparison between different techniques. This article is categorized under: Algorithmic Development > Text Mining Technologies > Machine Learning
Taxonomy of QA techniques provided in this paper
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Architecture of RE2 model(Yang et al., 2019)
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Architecture of BiMPM‐matching strategies (Wang et al., 2017)
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Architecture of Comp‐Clip model (Yoon et al., 2019)
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Architecture of CETE model (Laskar et al., 2020)
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Computation flow of GSAMN (Lai et al., 2019)
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NLP‐capsule framework (Zhao et al., 2019)
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General architecture of text‐based QA (Jurafsky & Martin, 2009)
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Technologies > Machine Learning
Algorithmic Development > Text Mining

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