Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans . |
| Approach: | They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
| Outcome: | The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points. |
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| Challenge: | Existing work on cross-sentence relation extraction is limited to three consecutive sentences, which severely limits recall. |
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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. |
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Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
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Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)
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| Challenge: | Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text. |
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| Challenge: | Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE. |
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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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Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)
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| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
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Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision (L18-1)
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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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TTM-RE: Memory-Augmented Document-Level Relation Extraction (2024.acl-long)
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| Challenge: | Existing methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. |
| Approach: | They propose a novel approach that integrates a trainable memory module with a noisy-robust loss function that accounts for the positive-unlabeled setting to unlock the full potential of large-scale noisy training data. |
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Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
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