LSTM Recurrent Neural Networks for Short Text and Sentiment Classication文章阅读笔记
一、文本预处理 1、删除所有符号，例如:% 2、所有数字用例如'SPEC——NUM'来代替 3、为数据集创建词典 二、词向量化 采用one-hot来表示单词 三、词典大小 不需太大，原文中的词典大小是9000 四、数据集样本数不平衡 原文中Bad和Neutal类的样本数远远少于Good分类，因此将Bad和Neutral的样本数扩大了一倍 四、LSTM、BLSTM、GRU对比 it has bee
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相关文章
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