Papers › Graph-based Dependency Parsing with Graph Neural Networks
Graph-based Dependency Parsing with Graph Neural Networks
Tao Ji, Yuanbin Wu, Man Lan
We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN{'}s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0{\%}, 94.3{\%}) among systems without using any external resources.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dependency Parsing | Penn Treebank | Graph-based parser with GNNs | LAS | 94.31 | #12 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Graph-based parser with GNNs | POS | 97.3 | #12 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Graph-based parser with GNNs | UAS | 95.97 | #12 of 22 | 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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