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.
P. Frischmuth, S. Auer, S. Tramp, J. Unbehauen, K. Holzweißig, and C. Marquardt. Proceedings of the Workshop on Semantic Web Enterprise Adoption and
Best Practice Co-located with 12th International Semantic Web Conference
(ISWC 2013), Sydney, Australia, October 22, 2013, volume 1106 of CEUR Workshop Proceedings, CEUR-WS.org, (2013)
D. Kontokostas, A. Zaveri, S. Auer, and J. Lehmann. Knowledge Engineering and the Semantic Web - 4th International Conference,
KESW 2013, St. Petersburg, Russia, October 7-9, 2013. Proceedings, volume 394 of Communications in Computer and Information Science, page 265--272. Springer, (2013)
A. Khalili, and S. Auer. Web Information Systems Engineering - WISE 2013 - 14th International
Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part
II, volume 8181 of Lecture Notes in Computer Science, page 425--438. Springer, (2013)