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• W2612773933 abstract "We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs." @default.
• W2612773933 created "2017-05-19" @default.
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• W2612773933 date "2017-01-01" @default.
• W2612773933 modified "2023-10-17" @default.
• W2612773933 title "Deep Joint Entity Disambiguation with Local Neural Attention" @default.
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• W2612773933 doi "https://doi.org/10.18653/v1/d17-1277" @default.
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