Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information, in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.

采用标签-词汇联合嵌入法和注意力机制的词嵌入模型，用于文本分类并保持了词嵌入的可解释性，同时提供了利用其他信息的能力。在多个大型文本数据集上实验表明，该方法在准确率和速度方面均优于现有最先进方法。

文字分类的词与标签联合嵌入