Part of the allure of classifying things by assigning tags to them is that the user can give free reign to sloppiness. There is no authority —human or computational— passing judgment on the appropriateness or validity of tags, because tags have to mak
Clarity regarding controlled vocabularies, taxonomies, thesauri, ontologies, and metamodels. With all the scuttlebut going around about folksonomies and tagging, these are important terms to understand. In the process of tagging, it's pretty noticeable
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.
H. Dong, W. Wang, K. Huang, und F. Coenen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Seite 1348--1354. Minneapolis, Minnesota, Association for Computational Linguistics, (Juni 2019)
S. Pandya, P. Virparia, und R. Chavda. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 5 (1):
09 - 15(Februar 2016)