  Extreme multi-label text classification (XMTC) aims at tagging a document
with most relevant labels from an extremely large-scale label set. It is a
challenging problem especially for the tail labels because there are only few
training documents to build classifier. This paper is motivated to better
explore the semantic relationship between each document and extreme labels by
taking advantage of both document content and label correlation. Our objective
is to establish an explicit label-aware representation for each document with a
hybrid attention deep neural network model(LAHA). LAHA consists of three parts.
The first part adopts a multi-label self-attention mechanism to detect the
contribution of each word to labels. The second part exploits the label
structure and document content to determine the semantic connection between
words and labels in a same latent space. An adaptive fusion strategy is
designed in the third part to obtain the final label-aware document
representation so that the essence of previous two parts can be sufficiently
integrated. Extensive experiments have been conducted on six benchmark datasets
by comparing with the state-of-the-art methods. The results show the
superiority of our proposed LAHA method, especially on the tail labels.
