Papers with ad-hoc post-processing
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss (N18-1)
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| Challenge: | Existing methods for fine-grained type classification rely on distant supervision and are susceptible to noisy labels that can be out-of-context or overly-specific. |
| Approach: | They propose a neural network model that uses cross-entropy loss function to handle out-of-context labels and hierarchical loss normalization to cope with overly-specific ones. |
| Outcome: | The proposed model outperforms the state-of-the-art on established benchmarks for the task. |