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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (12): 21-29    DOI: 10.11925/infotech.2096-3467.2019.0267
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Classifying Short Texts with Improved-Attention Based Bidirectional Long Memory Network
Zhiyong Tao1,Xiaobing Li1,2(),Ying Liu1,Xiaofang Liu1
1 School of Electronic and Information Engineering, Liaoning Technical University, Huludao125105, China
2 Fuxin Lixing Technology Co., Ltd., Fuxin 123000, China
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Abstract  

[Objective] This paper proposes a new model based on bidirectional long-short term memory network with improved attention, aiming to address the issues facing short texts classification. [Methods] First, we used the pre-trained word vectors to digitize the original texts. Then, we extracted their semantic features with bidirectional long-short term memory network. Third, we calculated their global attention scores with the fused forward and reverse features in the improved attention layer. Finally, we obtained short texts vector representation with deep semantic features. [Results] We used Softmax to create the sample label. Compared with the traditional CNN, LSTM and BLSTM networks, the proposed model improved the classification accuracy up to 19.1%. [Limitations] The performance of our new model on long texts is not satisfactory. [Conclusions] The proposed model could effectively classify short texts.

Key wordsShort Text Classification      Bidirectional Long-short Term Memory Network      Attentive Mechanism     
Received: 07 March 2019      Published: 25 January 2020
ZTFLH:  TP391.9  
Corresponding Authors: Xiaobing Li     E-mail: lixiaobing_lgd@163.com

Cite this article:

Zhiyong Tao,Xiaobing Li,Ying Liu,Xiaofang Liu. Classifying Short Texts with Improved-Attention Based Bidirectional Long Memory Network. Data Analysis and Knowledge Discovery, 2019, 3(12): 21-29.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0267     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I12/21

数据集 类别 样本数 训练集 验证集 测试集 平均词数 文本最大长度 词语总数
Chinese_news (CNH) 18 192 000 156 000 18 000 18 000 12 29 137 890
MR 2 10 658 7 462 1 598 1 598 20 57 18 159
TREC 6 5 949 5 357 - 592 10 35 9 337
IMDB 2 50 000 25 000 12 500 12 500 239 2 525 141 902
IMDB_10 10 50 000 25 000 12 500 12 500 239 2 525 141 902
Yelp 5 35 000 25 000 5 000 5 000 129 984 104 352
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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