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…
SimPack is intended primarily for the research of similarity between concepts in ontologies or ontologies as a whole. Possible other application areas of SimPack include
Semantic similarity and relatedness measures assess how alike two words are within a language and are playing an important role in the development of the Semantic Web. This thesis research advances the knowledge of existing similarity and relatedness measures. A generalized tool to experiment with semantic similarity and relatedness measures in a variety of ontological terminologies has been developed using the Simple Knowledge Organization System (SKOS), a proposed W3C standard for the Semantic Web. SKOS represents a terminology or domain vocabulary in a machine-understandable way. A flexible conversion tool is used to convert any vocabulary in the Unified Medical Language System (UMLS) Metathesaurus and OWL ontologies into an extended SKOS ontological terminology. The generalized tool for measuring semantic similarity and relatedness is then used to analyze a wide variety of semantic similarity measures and new set-based relatedness measures on three major vocabularies of the UMLS Metathesaurus.
This specification defines a small ontology for similarity called MuSim. In MuSim, the association between two (or more) Things is a class to be reified rather than a property. This allows us to embrace the complexity of associations and accommodate the subjectivity and context-dependence of musical and multimedia similarity. Although this ontology was designed with music similarity in mind, it can readily be applied to other domains.
A Java implementation of WordNet::Similarity - a Perl coded package that allows one to measure, in various ways, the similarity between word senses using the structure of WordNet.
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)
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)
A. Correya, R. Hennequin, and M. Arcos. (2018)cite arxiv:1808.10351Comment: Music Information Retrieval, Cover Song Identification, Million Song Dataset, Natural Language Processing.
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657-672(January 2017)
A. Correya, R. Hennequin, and M. Arcos. (2018)cite arxiv:1808.10351Comment: Music Information Retrieval, Cover Song Identification, Million Song Dataset, Natural Language Processing.
A. Vaglio, R. Hennequin, M. Moussallam, and G. Richard. Proceedings of the 22nd International Society for Music Information Retrieval Conferenc, Society for Music Information Retrieval, (November 2021)
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C. Tsai, and P. Brusilovsky. Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, page 22--30. New York, NY, USA, ACM, (2019)
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