Label-Attentive Hierarchical Attention Network for Text Classification

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Abstract

The performance of text classification largely hinges on features extraction from text through representation learning. Recently, many deep learning models have been proved to be the state-of-the-art method for text classification. However, those approaches always ignore exploiting global clues, such as label information, which result in partial semantic loss. This paper proposes a text classification framework called Label-Attentive Hierarchical Attention Network (LAHAN). Firstly, to exploit global clues better and avoid semantic loss between text and label information, LAHAN generates label-attentive embedding by introducing the joint features of text words and labels information. Then a hierarchical architecture is adopted to utilize the label information on both word level and sentence level, which brings improvement to extract a better hierarchical text representation. Finally, the classification layer of our model predicts the category based on the full-text label-attentive representations. Experimental results on the several benchmark datasets demonstrate that our proposed method achieves improvement on accuracy compared to other state-of-the-art baselines.

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