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recursive neural network
A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structure, to produce a structured prediction over variable-length input, or a scalar prediction on it, by traversing a given structure in topological order. RNNs have been successful in learning sequence and tree structures in natural language processing, mainly phrase and sentence continuous representations based on word embedding. RNNs have first been introduced to learn distributed representations of structure, such as logical terms.
== Architectures ==
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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