Papers › End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Makoto Miwa, Mohit Bansal
We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the state-of-the-art feature-based model on end-to-end relation extraction, achieving 12.1% and 5.7% relative error reductions in F1-score on ACE2005 and ACE2004, respectively. We also show that our LSTM-RNN based model compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Finally, we present an extensive ablation analysis of several model components.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | ACE 2004 | SPTree | Cross Sentence | No | #6 of 11 | Archive leaderboard | report |
| Relation Extraction | ACE 2004 | SPTree | NER Micro F1 | 81.8 | #6 of 11 | Archive leaderboard | report |
| Relation Extraction | ACE 2004 | SPTree | RE+ Micro F1 | 48.4 | #6 of 11 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | SPTree | Cross Sentence | No | #21 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | SPTree | NER Micro F1 | 83.4 | #21 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | SPTree | RE+ Micro F1 | 55.6 | #21 of 30 | Archive leaderboard | report |
| Relation Extraction | ACE 2005 | SPTree | Sentence Encoder | biLSTM | #21 of 30 | Archive leaderboard | report |
| Relation Extraction | NYT11-HRL | SPTree | F1 | 53.1 | #8 of 12 | 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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