2022
DOI: 10.3390/e24070964
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Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks

Abstract: Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. Specifically, we propose a novel deep neural model called CopyFet for FET via a copy-… Show more

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