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.
D. Benz, B. Krause, G. Kumar, A. Hotho, and G. Stumme. Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009), Bled, Slovenia, (September 2009)
D. Benz, B. Krause, G. Kumar, A. Hotho, and G. Stumme. Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009), Bled, Slovenia, (September 2009)
S. Braun, and A. Schmidt. 8th International Conference on the Design of Cooperative Systems (COOP '08), Carry-le-Rouet, France, May 20-23, 2008, (2008)
S. Braun, A. Schmidt, A. Walter, G. Nagypal, and V. Zacharias. Proceedings of the Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at the 16th International World Wide Web Conference (WWW2007) Banff, Canada, May 8, 2007, volume 273 of CEUR Workshop Proceedings, (2007)