基于注意力机制的文本作者识别

基于神经网络的作者识别在面临较多候选作者时识别准确率会大幅降低。为了提高作者识别精度，提出一种由快速文本分类（fastText）和注意力层构成的神经网络，并将该网络结合连续的词性标签n元组合（POS n-gram）特征进行中文小说的作者识别。与文本卷积神经网络（TextCNN）、文本循环神经网络（TextRNN）、长短期记忆（LSTM）网络和fastText进行对比，实验结果表明，所提出的模型获得了最高的分类准确率，与fastText模型相比，注意力机制的引入使得不同POS n-gram特征对应的准确率平均提高了2.14个百分点；同时，该模型保留了fastText的快速高效，且其所使用的文本特征可以推广到其他语言上。

Authorship identification of text based on attention mechanism

The accuracy of authorship identification based on deep neural network decreases significantly when faced with a large number of candidate authors. In order to improve the accuracy of authorship identification, a neural network consisting of fast text classification (fastText) and an attention layer was proposed, and it was combined with the continuous Part-Of-Speech (POS) n-gram features for authorship identification of Chinese novels. Compared with Text Convolutional Neural Network (TextCNN), Text Recurrent Neural Network (TextRNN), Long Short-Term Memory (LSTM) network and fastText, the experimental results show that the proposed model obtains the highest classification accuracy. Compared with the fastText model, the introduction of attention mechanism increases the accuracy corresponding to different POS n-gram features by 2.14 percentage points on average; meanwhile, the model retains the high-speed and efficiency of fastText, and the text features used by it can be applied to other languages.