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To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models ...
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RNN Classification of English Vowels: Nasalized or Not
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In: Proceedings of the Society for Computation in Linguistics (2019)
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The focal alteration and causal connectivity in children with new-onset benign epilepsy with centrotemporal spikes
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Resource Description Framework
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Abstract:
The Resource Description Framework (RDF) is a graph-based data model for sharing information on the Web. RDF has been standardized by the World Wide Web Consortium (W3C). The data model puts special emphasis on the globally unique identification of all model elements with Uniform Resource Identifiers (URIs). The globally unique identification of the described objects (called resources in the RDF context) enables data from different sources to be represented in a single RDF graph and lays the foundation for the integration of the data in a pay-as-you-go fashion. The globally unique identification of schema elements (called vocabulary terms in the RDF context) provides for mixing terms from different schemata in a single RDF graph and makes RDF a schema-less data model. The RDF data model forms the foundation for a wide range of RDF-based technologies including the SPARQL query language, various data exchange formats such as RDF/XML, RDFa, JSON-LD, and Turtle, as well as RDF vocabularies for data interchange (FOAF, Dublin Core, Schema.org, Open Graph Protocol, Data Cube, Provenance Vocabulary) and knowledge representation (SKOS, RDF Schema, OWL).
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Keyword:
004 Informatik
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URL: https://link.springer.com/referenceworkentry/10.1007%2F978-1-4614-8265-9_905 https://madoc.bib.uni-mannheim.de/50975/ https://doi.org/10.1007/978-1-4614-8265-9_905
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Studies on the Differences Between Chinese and Western Nature Poems
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In: Studies in Literature and Language; Vol 10, No 3 (2015): Studies in Literature and Language; 83-88 ; 1923-1563 ; 1923-1555 (2015)
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Prediction of age, sentiment, and connectivity from social media text
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