| Challenge: | Existing methods for Relation Extraction (RE) annotations use links between entities . a domain link connects the relation mention to the source entity while a range link connect the relation to the destination entity. |
| Approach: | They propose an Ontology-Style Relation (OSR) annotation approach to find relation mentions in relation annotations. |
| Outcome: | The proposed approach can be easily converted to Ontology RDF triples to populate an Ontologies. |
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AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)
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| Challenge: | Existing in-context learning methods for relation extraction often overlook entity relationships . Existing methods for RE prioritize language similarity over structural similarity . |
| Approach: | They propose an AMR-enhanced retrieval-based ICL method for relation extraction . their method retrieves in-context examples based on semantic structure similarity . |
| Outcome: | The proposed method outperforms baselines on four English RE datasets and in the more demanding unsupervised setting. |
CrossRE: A Cross-Domain Dataset for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Relation Extraction (RE) evaluation is limited to in-domain setups . despite the drought of research on cross-domain RE, its practical importance remains . |
| Approach: | They propose a cross-domain benchmark for relation extraction which includes multi-label annotations and meta-data to include explanations and flags of difficult instances. |
| Outcome: | The proposed model includes explanations and flags of difficult instances. |
The State of Relation Extraction Data Quality: Is Bigger Always Better? (2024.findings-acl)
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| Challenge: | Relation extraction (RE) methods extract tuples of relationships from text . many datasets with frequent label errors have been used . |
| Approach: | They review recent surveys and a sample of recent RE methods papers . they find that real-time evaluations of RE methods are possible . |
| Outcome: | a sample of 38 datasets currently being used shows that many have frequent label errors . a small number of relations in specific domains can more realistically evaluate methods . |
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning (2021.eacl-main)
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| Challenge: | Using a multi-task approach, we extract facts from documents at entity level. |
| Approach: | They propose a multi-task approach that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. |
| Outcome: | The proposed model is on par with task-specific learning, though more efficient due to shared parameters and training steps. |
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
| Approach: | They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE . |
| Outcome: | The proposed methods can extract relational facts from text, but they are still lacking in the current field. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Relating Relations: Meta-Relation Extraction from Online Health Forum Posts (2021.eacl-srw)
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| Challenge: | Relation extraction is a key task in knowledge extraction, and is often defined as identifying relations that hold between entities in text. |
| Approach: | They propose to conceptualise relation extraction tasks for user-generated health texts and create a dataset and model for meta-relation extraction. |
| Outcome: | The proposed model will be able to extract meta-relations from user-generated health texts with tolerable cognitive load and a new dataset and annotation scheme with tolerance for annotations. |
Learning Relatedness between Types with Prototypes for Relation Extraction (2021.eacl-main)
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| Challenge: | Existing datasets have no intrinsic Ontology for relation types. |
| Approach: | They propose to use prototypical examples to represent each relation type and use them to augment related types from a different dataset. |
| Outcome: | The proposed model improves on a baseline with multi-task learning between datasets to obtain better representation for relations. |
VER: Unifying Verbalizing Entities and Relations (2023.findings-emnlp)
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| Challenge: | a new model for verbalizing entities and relations is proposed to help understand entities and relationships . a unified model for Verbalizing Entities and Relations is proposed . |
| Approach: | They propose a model that takes any entity or entity set as input and generates a sentence to represent entities and relations. |
| Outcome: | The proposed model can generate sentences describing entities and relations . it can be used to explain entities and relationships, and to perform commonsense reasoning tasks . |
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. |
| Outcome: | The proposed method can extract relations from document-level relation datasets with minimal human effort. |