Deep Neural Networks for Relation Extraction
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Abstract (arXiv)
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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References · 10
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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- 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
- 2017Cross-Sentence N-ary Relation Extraction with Graph LSTMscited by 101
- 2018Few-shot classification in Named Entity Recognition Taskcited by 64
- 2019Neural Architectures for Nested NER through Linearizationcited by 56
- 2019Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphscited by 53
- 2019Fine-tune Bert for DocRED with Two-step Processcited by 42
- 2019Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentionscited by 27
- 2018End-to-end neural relation extraction using deep biaffine attentioncited by 19
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