https://www.bibsonomy.org/tag/subsumptionBibSonomy RSS Feed for /tag/subsumptionhttps://github.com/jingcshi/bert_subsumptionhangdong2023-10-13T12:10:21+02:00e-commerce ontology_learning subsumption <a itemprop="url" data-versiondate="2023-10-13T12:10:21+02:00" href="https://github.com/jingcshi/bert_subsumption" rel="nofollow" class="description-link">https://github.com/jingcshi/bert_subsumption</a>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.https://cgi.csc.liv.ac.uk/~hang/pdf/social_anno_multilabel_camera_ready.pdfhangdong2019-03-25T09:36:44+01:00attention_mechanisms attention_network bigru folksonomy gru han hierarchical_attention information_organisation jman knowledge mll multi-label multi-label_classification myown regularisation regularisers semantic_relations semantics similarity social_media social_tag social_tagging_data social_tags subsumption tag <span itemprop="description">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.</span>