Label-Attentive Hierarchical Network for Document Classification

Abstract

Text classification is one of the most fundamental and important tasks in the field of natural language processing, which aims to identify the most relevant label for a given piece of text. Although deep learning-based text classification methods have achieved promising results, most researches mainly focus on the internal context information of the document, ignoring the available global information such as document hierarchy and label semantics. To address this problem, we propose a novel Label-Attentive Hierarchical Network (LAHN) for document classification. In particular, we integrate label information into the hierarchical structure of the document by calculating the word-label attention at word level and the sentence-label attention at sentence level respectively. We give full consideration to the global information during encoding the whole document, which makes the final document representation vector more discriminative for classification. Extensive experiments on several benchmark datasets show that our proposed LAHN surpasses several state-of-the-art methods.