Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (2020.coling-main)
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| Challenge: | Existing graph convolutional networks use pruned dependency trees to filter irrelevant nodes from sentence graphs. |
| Approach: | They propose to construct multiple sub-graphs from shortest dependency path and words linked to entities in the dependency parse to obtain more informative features useful for relation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on a sentence-level relation extraction dataset and the SemEval 2010 Task 8 sentence- level relation extraction data. |
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