Label-Aware Text Representation for Multi-Label Text Classification

Abstract

Multi-label text classification (MLTC) is an important task in natural language processing (NLP), which is appealing to researchers in both academia and industry. However, few of studies have been conducted on the relations among the labels. Most of existing methods tend to neglect the semantic information between labels and words. In this paper, we propose a label-aware network to obtain both the label correlation and text representation. A heterogeneous graph is built from words and labels to learn the label representation by metap-ath2vec, since two nearby labels or words in the graph have similar relation and the graph structure is beneficial for label representation as well. Each part of the text contributes differently to label inference, therefore bidirectional attention flow is exploited for label-aware text representation in two directions: from text to label and from label to text. Experimental evaluations illustrate that the proposed method outperforms various baselines on both offline benchmarks and real-world online systems.