Deep Neural Networks for Relation Extraction
This work addresses the problem of automatic knowledge base population for natural language processing applications, presenting incremental improvements in neural network-based relation extraction methods.
The thesis tackled relation extraction from text by proposing three neural network models: a syntax-focused multi-factor attention network for entity relations, two joint entity and relation extraction frameworks using encoder-decoder architecture, and a hierarchical entity graph convolutional network for cross-document relation extraction.
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.