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• W2741361404 abstract "Word embeddings are supposed to provide easy access to semantic relations such as “male of” (man–woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings." @default.
• W2741361404 created "2017-08-08" @default.
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• W2741361404 date "2017-01-01" @default.
• W2741361404 modified "2023-10-17" @default.
• W2741361404 title "Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible" @default.
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• W2741361404 doi "https://doi.org/10.18653/v1/s17-1012" @default.
• W2741361404 hasPublicationYear "2017" @default.
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