基于双向长短时记忆网络的改进注意力短文本分类方法 *
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陶志勇,李小兵,刘影,刘晓芳
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Classifying Short Texts with Improved-Attention Based Bidirectional Long Memory Network
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Zhiyong Tao,Xiaobing Li,Ying Liu,Xiaofang Liu
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表2 模型分类精度
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注意力 | 数据集 | CNH | MR | TREC | IMDB | IMDB_10 | Yelp | 未引入注意力 | CNN | 60.0% | 72.1% | 81.7% | 74.4% | 35.1% | 46.0% | LSTM | 75.3% | 73.7% | 85.4% | 88.5% | 40.3% | 55.3% | BLSTM_ave | 78.7% | 78.7% | 87.3% | 90.8% | 47.4% | 59.3% | BLSTM | 78.5% | 80.3% | 89.4% | 89.7% | 44.2% | 61.8% | 引入注意力 | ABLSTM | 78.7% | 80.7% | 89.0% | 91.5% | 46.8% | 62.3% | HAN | 79.0% | 80.3% | 89.0% | 90.2% | 49.4% | 62.1% | IABLSTM | 79.1% | 81.5% | 90.9% | 91.4% | 49.4% | 62.8% |
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