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Deep Neural Networks for Relation Extraction

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arxiv 2104.01799 v1 pith:HAMP74DO submitted 2021-04-05 cs.CL

classification cs.CL
keywords relationextractionproposeentitynetworkacrossarchitectureattention
verification ladder T0 review T1 audit T2 compute T3 formal
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Relation extraction from text is an important task for automatic knowledge base population. In this thesis, we first propose a syntax-focused multi-factor attention network model for finding the relation between two entities. Next, we propose two joint entity and relation extraction frameworks based on encoder-decoder architecture. Finally, we propose a hierarchical entity graph convolutional network for relation extraction across documents.

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