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Found 6 papers, 0 papers with code
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments
To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning.
Hierarchical Graph Convolutional Networks for Jointly Resolving Cross-document Coreference of Entity and Event Mentions
Prior work has demonstrated the benefits of the predicate-argument information and document context for resolving the coreference of event mentions.
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks
Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolution neural networks (GCN) to achieve state-of-the-art performance.
Event Detection: Gate Diversity and Syntactic Importance Scoresfor Graph Convolution Neural Networks
Recent studies on event detection (ED) haveshown that the syntactic dependency graph canbe employed in graph convolution neural net-works (GCN) to achieve state-of-the-art per-formance.
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction
The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word.
On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning
Deep learning models have achieved state-of-the-art performances on many relation extraction datasets.
