Neural Segmental Hypergraphs for Overlapping Mention Recognition
In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping mentions.
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Datasets
GENIA
ACE 2005
NNE
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Nested Mention Recognition | ACE 2004 | Neural segmental hypergraphs | F1 | 75.1 | # 6 | |
| Named Entity Recognition (NER) | ACE 2004 | Neural segmental hypergraphs | F1 | 75.1 | # 8 | |
| Multi-Task Supervision | n | # 1 | ||||
| Nested Named Entity Recognition | ACE 2004 | Neural segmental hypergraphs | F1 | 75.1 | # 22 | |
| Nested Named Entity Recognition | ACE 2005 | Neural segmental hypergraphs | F1 | 74.5 | # 22 | |
| Named Entity Recognition (NER) | ACE 2005 | Neural segmental hypergraphs | F1 | 74.5 | # 18 | |
| Nested Mention Recognition | ACE 2005 | Neural segmental hypergraphs | F1 | 74.5 | # 8 | |
| Nested Named Entity Recognition | GENIA | Neural segmental hypergraphs | F1 | 75.1 | # 21 | |
| Named Entity Recognition (NER) | GENIA | Neural segmental hypergraphs | F1 | 75.1 | # 9 |
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