| Challenge: | Distant supervised relation extraction suffers from mislabeling problem due to strong assumption . et al., 2009: distant supervision can be used to extract relations between entities . |
| Approach: | They propose a neural relation extraction framework which uses convolution and pooling to extract better semantic features of sentences. |
| Outcome: | The proposed framework improves relation extraction performance on a widely used dataset. |
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RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
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| Challenge: | Distantly-supervised Relation Extraction (RE) methods ignore readily available side information. |
| Approach: | They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor. |
| Outcome: | The proposed method improves performance even when limited side information is available. |
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (N19-1)
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| Challenge: | Existing distance supervised relation extraction models for long-tail data are inadequate for many applications. |
| Approach: | They propose to leverage implicit relational knowledge among class labels and learn explicit relational knowing using graph convolution networks. |
| Outcome: | The proposed approach outperforms baselines for long-tail relations on a large-scale dataset. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
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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. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
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. |
| Approach: | They propose a semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. |
| Outcome: | The proposed approach achieves significant improvements over baselines on real-world datasets. |
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. |
A Dataset for Inter-Sentence Relation Extraction using Distant Supervision (L18-1)
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| Challenge: | Existing methods for intra-sentence relation extraction use a distance supervision method to extract relations between entities. |
| Approach: | They propose a benchmark dataset for the task of inter-sentence relation extraction using relations previously used for intra-sentent relation extraction. |
| Outcome: | The proposed dataset is compared with baseline models and recurrent neural network models on the developed dataset. |
Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction (D19-1)
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| Challenge: | Distantly Supervised Relation Extraction (DSRE) suffers from mislabelled data . human annotation on large datasets is costly and often impossible . |
| Approach: | They propose a model that employs a collaborative curriculum learning framework to reduce mislabelled data. |
| Outcome: | The proposed model outperforms baselines including state-of-the-art in terms of P@N and PR curve metrics on a widely-used public dataset. |
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. |
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 . |
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 . |
| Outcome: | The proposed method can select reliable sentences via cross-platform collaboration. |