Name
Abstract | ||
|---|---|---|
Event causality identification is an important research task in natural language processing. Existing methods largely focus on identifying explicit causal relations, and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem and propose a novel document-level context-based graph inference mechanism. Specifically, we use attention-based neural networks to automatically extract document-level contextual information, and a direction-sensitive graph inference mechanism to achieve information transfer and interaction among event causalities. Experimental results on the EventStoryLine v1.5 dataset show that our approach outperforms previous methods and baseline systems by a large margin in F1-score metrics (2.45% improvement on intra-sentence causalities and 3.08% improvement on cross-sentence causalities). Further analysis demonstrates that our model can effectively capture the document-level contextual information and latent causal information among events. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2021.01.078 | Information Sciences |
Keywords | DocType | Volume |
Event causality,Neural networks,Attention mechanism,Deep learning,Natural language processing | Journal | 561 |
ISSN | Citations | PageRank |
0020-0255 | 2 | 0.40 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Zhao | 1 | 2 | 0.40 |
| Donghong Ji | 2 | 892 | 120.08 |
| Fazhi He | 3 | 540 | 41.02 |
| Yijiang Liu | 4 | 2 | 2.09 |
| Yafeng Ren | 5 | 102 | 13.57 |