| Challenge: | Existing methods for relation extraction use knowledge graphs to automatically label training data . but, it suffers from the wrong labeling problem because not all sentences containing two entities can express their relations in KGs . |
| Approach: | They propose a distant supervision approach to automatically label training instances . they integrate hierarchical information of relations into distantly supervised relation extraction . |
| Outcome: | The proposed model outperforms baseline models on a large-scale dataset. |
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| Challenge: | Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels. |
| Approach: | They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences. |
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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. |
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ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to find relational facts from texts lack hierarchical information of relations. |
| Approach: | They propose a hierarchical classification framework which extracts relation in a top-down manner. |
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Modeling Multi-Granularity Hierarchical Features for Relation Extraction (2022.naacl-main)
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| Challenge: | Existing work on relation extraction focuses on constructing explicit structured features using knowledge graph and dependency tree. |
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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. |
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Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
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| Challenge: | Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. |
| Approach: | They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion. |
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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. |
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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. |
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Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction (D18-1)
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| Challenge: | Existing approaches to label large-scale data are inadequate for distantly supervised relation extraction (DS-RE). |
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Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
| Approach: | They propose an automated annotation method that integrates an LLM and a natural language inference module to generate relation triples. |
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