Deep Neural Networks for Relation Extraction
arXiv:2104.01799
Abstract
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.
arXiv commentPhD Thesis, National University of Singapore (2020)
References in corpus (13)
- Distributed Representations of Words and Phrases and their Compositionality
- Bidirectional Attention Flow for Machine Comprehension
- Joint entity recognition and relation extraction as a multi-head selection problem
- SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
- ReasoNet: Learning to Stop Reading in Machine Comprehension
- Fine-tune Bert for DocRED with Two-step Process
- End-to-end neural relation extraction using deep biaffine attention
- Entity-Relation Extraction as Multi-Turn Question Answering
- BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
- Hierarchical Graph Network for Multi-hop Question Answering
- Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
- RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
- A Question-Focused Multi-Factor Attention Network for Question Answering