LSTM Recurrent Neural Networks for Short Text and Sentiment Classication文章閱讀筆記
一、文本預處理 1、刪除所有符號，例如:% 2、所有數字用例如'SPEC——NUM'來代替 3、爲數據集創建詞典 二、詞向量化 採用one-hot來表示單詞 三、詞典大小 不需太大，原文中的詞典大小是9000 四、數據集樣本數不平衡 原文中Bad和Neutal類的樣本數遠遠少於Good分類，因此將Bad和Neutral的樣本數擴大了一倍 四、LSTM、BLSTM、GRU對比 it has bee
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相關文章
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