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Chunking On Penn Treebank
Metrics
F1 score
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| ACE | 97.3 | Automated Concatenation of Embeddings for Structured Prediction | |
| Flair embeddings | 96.72 | Contextual String Embeddings for Sequence Labeling | - |
| JMT | 95.77 | A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks | |
| Low supervision | 95.57 | - | - |
| IntNet + BiLSTM-CRF | 95.29 | Learning Better Internal Structure of Words for Sequence Labeling | - |
| Suzuki and Isozaki | 95.15 | - | - |
| NCRF++ | 95.06 | NCRF++: An Open-source Neural Sequence Labeling Toolkit | |
| BI-LSTM-CRF (Senna) (ours) | 94.46 | Bidirectional LSTM-CRF Models for Sequence Tagging |
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