arXiv:2104.01799
공식 코드 · Papers with Code
Deep Neural Networks for Relation Extraction
원문 초록
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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Chaining Event Spans for Temporal Relation Grounding
Comparative Analysis of AI Agent Architectures for Entity Relationship Classification
Towards a More Generalized Approach in Open Relation Extraction
Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-training
토론
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