What is Semantic Similarity? Definition of Semantic Similarity: A concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning/semantic content. ( Wikipedia, 2012e ).
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.
What is Semantic Similarity? Definition of Semantic Similarity: A concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning/semantic content. ( Wikipedia, 2012e ).
Mean Absolute Error (MAE) and Root mean squared error (RMSE) are two of the most common metrics used to measure accuracy for continuous variables. Not sure if I’m imagining it but I think there used…
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S. Heil, K. Kopp, A. Zehe, K. Kobs, and A. Hotho. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), page 1190--1195. Seattle, United States, Association for Computational Linguistics, (July 2022)
D. Wangsadirdja, F. Heinickel, S. Trapp, A. Zehe, K. Kobs, and A. Hotho. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), page 1235--1243. Seattle, United States, Association for Computational Linguistics, (July 2022)
D. Wangsadirdja, F. Heinickel, S. Trapp, A. Zehe, K. Kobs, and A. Hotho. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), page 1235--1243. Seattle, United States, Association for Computational Linguistics, (July 2022)
S. Heil, K. Kopp, A. Zehe, K. Kobs, and A. Hotho. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), page 1190--1195. Seattle, United States, Association for Computational Linguistics, (July 2022)
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