Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field (2023.acl-long)
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| Challenge: | Existing approaches to joint Information Extraction (IE) neglect cross-instance or cross-task dependencies. |
| Approach: | They propose a joint IE framework that formulates joint 'conditional random field' to model cross-instance interactions . they incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method . |
| Outcome: | The proposed approach improves on three IE tasks compared with baseline and prior work. |
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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. |
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| Challenge: | Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances. |
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Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks (2021.naacl-main)
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| Challenge: | Existing work on information extraction (IE) has solved the four main tasks separately, thus failing to benefit from inter-dependencies between tasks. |
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| Challenge: | Existing work on IE tasks that use two types of dependencies is not optimal . emr, event trigger detection, event argument extraction, and relation extraction are challenging . |
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| Challenge: | Existing methods to extract entities and relations from unstructured texts are difficult to handle due to the overlapping triple problem. |
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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. |
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Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (D19-1)
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| Challenge: | Existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification. |
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
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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 . |
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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. |
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