FRINK Linked Data Fragments server

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• W2757369719 abstract "For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively “embed” entities that are new to the KB, and is able to link its mentions accurately." @default.
• W2757369719 created "2017-10-06" @default.
• W2757369719 creator A5005779128 @default.
• W2757369719 creator A5023802054 @default.
• W2757369719 creator A5053062856 @default.
• W2757369719 date "2017-01-01" @default.
• W2757369719 modified "2023-10-17" @default.
• W2757369719 title "Entity Linking via Joint Encoding of Types, Descriptions, and Context" @default.
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• W2757369719 doi "https://doi.org/10.18653/v1/d17-1284" @default.
• W2757369719 hasPublicationYear "2017" @default.
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