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- W3136332380 abstract "Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations." @default.
- W3136332380 created "2021-03-29" @default.
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- W3136332380 date "2021-03-15" @default.
- W3136332380 modified "2023-09-27" @default.
- W3136332380 title "Mention-centered Graph Neural Network for Document-level Relation Extraction." @default.
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