Papers › Efficient long-distance relation extraction with DG-SpanBERT
Efficient long-distance relation extraction with DG-SpanBERT
Jun Chen, Robert Hoehndorf, Mohamed Elhoseiny, Xiangliang Zhang
In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. Our DG-SpanBERT model inherits the advantage of SpanBERT on learning rich lexical features from large-scale corpus. It also has the ability to capture long-range relations between entities due to the usage of GCN on dependency tree. The experimental results show that our model outperforms other existing dependency-based and sequence-based models and achieves a state-of-the-art performance on the TACRED dataset.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | TACRED | DG-SpanBERT-large | F1 | 71.5 | #19 of 40 | Archive leaderboard | report |
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