BiSPN: Generating Entity Set and Relation Set Coherently in One Pass (2023.findings-emnlp)
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| Challenge: | Existing approaches to extract entities and relation triples from text are limited. |
| Approach: | They propose a bipartite set prediction network to generate entity set and relation set in parallel. |
| Outcome: | The proposed model can generate entity set and relation set in parallel, while maintaining coherence between the predicted entities and relation sets. |
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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. |
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 . |
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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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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 . |
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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. |
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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. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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| 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 . |
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GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction (2021.findings-emnlp)
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| Challenge: | Existing models for extracting relation triplets suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. |
| Approach: | They propose a special entity labelling method that fine-tunes the pre-trained model and learns the special entity labels simultaneously. |
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A Partition Filter Network for Joint Entity and Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract entity and relation feature are flawed because they do not consider the intimate connection between NER and RE. |
| Approach: | They propose a partition filter network to model two-way interaction between tasks . they leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition. |
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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. |
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