Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
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| Challenge: | Existing approaches for relation extraction (RE) use supervised learning on relation-specific training data, which is expensive to acquire. |
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from low quality of test set, which leads to considerable biased performance evaluation. |
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| Challenge: | Existing DS-NER approaches rely on large validation sets and test set for tuning inappropriately. |
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| Challenge: | Existing studies on DS-based relation extraction (RE) methods focus on handling label noise, but other factors may have been overlooked. |
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| Challenge: | Distant supervision is an efficient method for relation extraction, but it is noisy. |
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