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. |
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| Challenge: | Existing datasets for fine-grained entity typing are limited to English . a corpus of 4,800 mentions is manually labeled with free-form entity types . |
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| Challenge: | Existing approaches to fine-grained entity typing are limited by the errors in the annotation process. |
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