In this paper, we propose a dictionary screening method for embedding compression in text classification tasks. The key purpose of this method is to evaluate the importance of each keyword in the dictionary. To this end, we first train a pre-specified recurrent neural network-based model using a full dictionary. This leads to a benchmark model, which we then use to obtain the predicted class probabilities for each sample in a dataset. Next, to evaluate the impact of each keyword in affecting the predicted class probabilities, we develop a novel method for assessing the importance of each keyword in a dictionary. Consequently, each keyword can be screened, and only the most important keywords are reserved. With these screened keywords, a new dictionary with a considerably reduced size can be constructed. Accordingly, the original text sequence can be substantially compressed. The proposed method leads to significant reductions in terms of parameters, average text sequence, and dictionary size. Meanwhile, the prediction power remains very competitive compared to the benchmark model. Extensive numerical studies are presented to demonstrate the empirical performance of the proposed method.

本文提出了一种用于文本分类任务中的嵌入式压缩字典筛选方法，该方法使用特定的递归神经网络模型训练完整字典来评估每个关键字的重要性，并评估每个关键字对预测类别概率的影响来筛选最重要的关键字。得到一个极大程度压缩的新字典，从而大幅减小了原始文本序列的大小，同时保持了与基准模型相当的预测性能。

使用词典筛选的嵌入压缩进行文本分类