| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
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A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
| Outcome: | The proposed approach achieves an 8.16 speedup with a slight reduction in accuracy on standard benchmarks. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Distantly-Supervised Joint Extraction with Noise-Robust Learning (2024.findings-acl)
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| Challenge: | Existing approaches to identifying entity pairs and relations with a single model are noisy . Existing methods only consider one source of noise or make decisions using external knowledge . |
| Approach: | They propose a framework that aligns entity mentions with corresponding tags for joint extraction . they propose DENRL, which employs a lightweight transformer backbone for joint tagging . |
| Outcome: | The proposed framework outperforms baseline models on two benchmark datasets with better interpretability. |
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)
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| Challenge: | Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction. |
| Approach: | They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data. |
| Outcome: | The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks. |
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. |
Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)
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| Challenge: | a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction. |
| Approach: | They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction . |
| Outcome: | The proposed model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
| Outcome: | Empirical results show that the proposed model improves on two real-world datasets. |
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies (2022.naacl-main)
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| Challenge: | Existing models that perform information extraction tasks manually assume heuristic dependency between the task instances and mean-field factorization for the joint distribution of instance labels. |
| Approach: | They propose to induce a dependency graph among task instances to boost representation learning by estimating their joint distribution via Conditional Random Fields. |
| Outcome: | The proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages. |
READ: Improving Relation Extraction from an ADversarial Perspective (2024.findings-naacl)
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| Challenge: | Recent work in relation extraction (RE) has high generalization capability, but adversarial training methods rely on entities. |
| Approach: | They propose an adversarial training method specifically designed for relation extraction that introduces sequence- and token-level perturbations to the sample and uses a separate perturbation vocabulary to improve the search for entity and context perturbations. |
| Outcome: | The proposed method significantly improves accuracy and robustness in low-resource scenarios. |
Learning from Noisy Labels for Entity-Centric Information Extraction (2021.emnlp-main)
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| Challenge: | Recent information extraction approaches can easily overfit noisy labels and suffer from performance degradation. |
| Approach: | They propose a co-regularization framework for entity-centric information extraction that optimizes neural models with task-specific losses and regularizes them to generate similar predictions based on agreement loss. |
| Outcome: | The proposed framework is optimized with task-specific losses and generates similar predictions based on agreement loss. |