| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
| Approach: | They propose to embed relations with global statistics of relations to combat the wrong labeling problem of distant supervision. |
| Outcome: | The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models. |
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| Challenge: | Existing approaches to relation extraction (RE) only extract relations from sentences that contain two target entities. |
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Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
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Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
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Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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Revisiting Distant Supervision for Relation Extraction (L18-1)
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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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Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
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Distantly Supervised Relation Extraction in Federated Settings (2021.findings-emnlp)
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| Challenge: | Existing methods to label training datasets using distant supervision are expensive and cannot cover all walks of life. |
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A Dataset for Inter-Sentence Relation Extraction using Distant Supervision (L18-1)
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