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.
The java objects instantiated for JSP Custom Tags can now be pooled and reused. This significantly boosts the performance of JSP pages which use custom tags.
That page also says that web.xml can contain an "enablePooling" option for that, and that its default value is true.
After the doEndTag invocation, the tag handler is available for further invocations (and it is expected to have retained its properties).
So what I do is reset all local variables to their default value just before doEndTag() returns
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