Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction (D19-1)
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| Challenge: | Existing methods to construct noisy labeled data for relation extraction (RE) are expensive and lacks the labeling capability. |
| Approach: | They propose a 2-hop DS strategy to enhance distantly supervised relation extraction (RE) by combining sentences that mention entities that are linked to each other. |
| Outcome: | The proposed method outperforms baselines on a benchmark dataset by a substantial margin. |
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| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
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GAN Driven Semi-distant Supervision for Relation Extraction (N19-1)
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| Challenge: | Existing methods for relation extraction are limited to costly hand-labeled training sets and hard to be extended to large-scale relations. |
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Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)
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| Challenge: | Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming. |
| Approach: | They propose a dual supervision framework which utilizes both types of data to train relation extraction models. |
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Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)
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| Challenge: | Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems. |
| Approach: | They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling. |
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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. |
| Approach: | They propose a federated denoising framework to suppress label noise in federation . they propose to use a multiple instance learning based denoisation method to select reliable sentences . |
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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. |
| Approach: | They propose to use a new testing dataset to re-examine distant supervision approaches . they aim to draw new conclusions based on the new testing data . |
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Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
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| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
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Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)
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| Challenge: | Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents. |
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DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (P18-1)
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| Challenge: | Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. |
| Approach: | They propose a sentence-level true-positive generator to learn a true-negative generator from a fuzzy sentence bag. |
| Outcome: | The proposed method significantly improves the performance of distant supervision relation extraction compared to state-of-the-art systems. |
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning (P18-1)
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| Challenge: | Distant supervision is an efficient method for relation extraction, but it is noisy. |
| Approach: | They propose a deep reinforcement learning strategy to generate false-positive indicators . they redistribute false positives into negative examples to reduce false positive problem . |
| Outcome: | The proposed method significantly improves the performance of distant supervision compared to state-of-the-art systems. |