| 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. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
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Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision (2023.findings-eacl)
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| Challenge: | Existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. |
| Approach: | They propose a weighted contrastive learning method that explicitly reduces noise . they leverage supervised data to estimate reliability and reduce noise compared to non-weighted baselines . |
| Outcome: | The proposed method reduces the noise of distant supervision and estimates reliability of pre-training instances. |
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. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (N19-1)
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| Challenge: | Existing methods to extract relational data generated by distant supervision generate noisy training data. |
| Approach: | They propose a neural relation extraction method to deal with noisy training data generated by distant supervision. |
| Outcome: | Experimental results show that the proposed method is more accurate than state-of-the-art methods on the New York Times dataset. |
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. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
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 . |
| Outcome: | The proposed method can generate training data without noise and bias issues . the proposed method is annotated by the researchers on Amzaon Mechanical Turk . |
Enhanced Distant Supervision with State-Change Information for Relation Extraction (2022.lrec-1)
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| Challenge: | Existing methods for enhancing distant supervision with state-change information for relation extraction are limited. |
| Approach: | They propose a method for enhancing distant supervision with state-change information for relation extraction by adding temporal information to a curation dataset. |
| Outcome: | The proposed method reduces noise when used for static relation extraction and can be used to train a relation-extraction system that detects a change of state in relations. |
Global Relation Embedding for Relation Extraction (N18-1)
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| 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. |
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. |
| Approach: | They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks. |
| Outcome: | The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark. |
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)
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| Challenge: | Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences . |
| Approach: | They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase. |
| Outcome: | The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods. |