请问p71_TextRCNN_model.py中rnn-cnn layer定义原理是什么？最后ensemble left, embedding, right to output的方式不像Bi-LSTM layer呀

[brightmart]

this is just like taking context information(from left and right) into considering when encode a position to get rich information.

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发件人: JepsonWong notifications@github.com 发送时间: 2018年5月8日 10:35 收件人: brightmart/text_classification 抄送: Subscribed 主题: [brightmart/text_classification] 请问p71_TextRCNN_model.py中rnn-cnn layer定义原理是什么？最后ensemble left, embedding, right to output的方式不像Bi-LSTM layer呀 (#51)

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[fei161]

The meaning of the paper should be two layers of lstm followed by cnn layer features, why is there no lstm and cnn in your code?

[JepsonWong]

When you get context right, you reverse the input sequences, but you don't reverse the output sequences. I think the output seqences should be reversed. By the way, do you implement a rnn cell by yourself in your source code? So you don't use the tensorflow's api. thanks.@brightmart

[JepsonWong]

I guess the author implement a rnn cell in his source code. https://github.com/brightmart/text_classification/blob/a552e115a54b3d3f0736d418bd1b41a30550815b/a04_TextRCNN/p71_TextRCNN_model.py#L51 this is the rnn weights. @fei161

[fei161]

The author first makes a full connection to the context. Then concatenates the context vector concat and maximizes it. Finally connects to the full connection layer. @JepsonWong

[JepsonWong]

yes, i know this. I just think that when we get context right, we should reverse the output sequences. @fei161