We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semanticbased loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time.
M. Atzmueller, J. Baumeister, and F. Puppe. Medical Data Analysis, Proc. 4th Intl. Symposium on Medical Data Analysis (ISMDA 2003), LNCS 2868, page 23-30. (2003)
M. Atzmueller, J. Baumeister, and F. Puppe. Artificial Intelligence in Medicine. Special Issue on Intelligent Data Analysis in Medicine, 37 (1):
19--30(2006)
M. Atzmueller, J. Baumeister, and F. Puppe. Proc. 19th International Florida Artificial Intelligence Research Society Conference 2006 (FLAIRS-2006), page 402--407. AAAI Press, (2006)