The auto-question answering system based on convolution neural network

JING Li-jiao; FU Yun-bin; DONG Qi-wen

doi:10.3969/j.issn.1000-5641.2017.05.007

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JING Li-jiao, FU Yun-bin, DONG Qi-wen. The auto-question answering system based on convolution neural network[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 66-79. doi: 10.3969/j.issn.1000-5641.2017.05.007

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JING Li-jiao, FU Yun-bin, DONG Qi-wen. The auto-question answering system based on convolution neural network[J]. Journal of East China Normal University (Natural Sciences), 2017, (5): 66-79. doi: 10.3969/j.issn.1000-5641.2017.05.007

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The auto-question answering system based on convolution neural network

doi: 10.3969/j.issn.1000-5641.2017.05.007

School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received Date: 2017-06-23
Publish Date: 2017-09-25

Abstract

Abstract

The question-answering is a hot research field in natural language processing, which can give users concise and precise answer to the question presented in natural language and provide the users with more accurate information service. There are two key questions to be solved in the question answering system:one is to realize the semantic representation of natural language question and answer, and the other is to realize the semantic matching learning between question and answer. Convolution neural network is a classic deep network structure which has a strong ability to express semantics in the field of natural language processing in recent years, and is widely used in the field of automatic question and answer. This paper reviews some techniques in the question answering system that is based on the convolution neural network, the paper focuses on the knowledge-based and the text-oriented Q & A techniques from the two main perspectives of semantic representation and semantic matching, and indicates the current research difficulties.
- convolution neural network,
- automatic question answering,
- semantic representation,
- semantic matching

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References(57)

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