Papers › DGCN Based Solution for Entity Linking on Visual Rich Document
DGCN Based Solution for Entity Linking on Visual Rich Document
Shaodong Hou
Various works on entity extraction on visual rich document (VRD) have been done. However, few methods have been explored to handle entity linking problem. The difficulties come from the number of possible linking edges among entities is of square times complexity. Our approach introduces directed graph based convolutional network (DGCN) to predict relations between entities, which out performs existing methods on the FUNSD entity linking task.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Entity Linking | FUNSD | SINGU_GROUP | F1 | 70.51 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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