70 citations · 97 across the 9 of their papers we have counts for
4 papers · 2 filters
InducT-GCN: Inductive Graph Convolutional Networks for Text Classification
Kunze Wang, Soyeon Caren Han, Josiah Poon
Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) to capture the global word co…
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Kunze Wang, Soyeon Caren Han, Siqu Long +1
Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks.…
Understanding Graph Convolutional Networks for Text Classification
Soyeon Caren Han, Zihan Yuan, Kunze Wang +2
Graph Convolutional Networks (GCN) have been effective at tasks that have rich relational structure and can preserve global structure information of a dataset in graph embeddings.…
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
Yubo Ma, Zehao Wang, Yixin Cao +4
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there i…