Papers with SciERC
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. |
UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)
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| Challenge: | Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks . |
| Approach: | They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label. |
| Outcome: | The proposed model achieves competitive accuracy with the best extractor and is faster. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
| Approach: | They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation. |
| Outcome: | The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods . |
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
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| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
| Outcome: | The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset. |
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information (2020.emnlp-main)
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| Challenge: | Existing pre-trained models do not handle text spans and relation among text span pairs. |
| Approach: | They propose to integrate span-related information into pre-trained encoder for entity relation extraction task. |
| Outcome: | The proposed pre-training method outperforms distantly supervised pre-trained models on two entity relation extraction benchmark datasets. |
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. |
CARE: Co-Attention Network for Joint Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Existing joint entity and relation extraction methods suffer from feature confusion or inadequate interaction between the two subtasks. |
| Approach: | They propose a Co-Attention network for joint entity and relation extraction that adopts a parallel encoding strategy to learn separate representations for each subtask. |
| Outcome: | The proposed model outperforms existing models on three datasets . it uses a parallel encoding strategy to learn separate representations for each subtask . |
R-GDA: Reflective Guidance Data Augmentation with Multi-Agent Feedback for Domain-Specific Named Entity Recognition (2026.findings-eacl)
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| Challenge: | Named Entity Recognition (NER) tasks require data augmentation due to the scarcity of annotated corpora. |
| Approach: | They propose a framework that introduces a multi-agent feedback loop to enhance augmentation quality. |
| Outcome: | The proposed framework improves on SciERC and NCBI-disease datasets and achieves low BERTScore in most cases. |
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (D18-1)
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| Challenge: | Existing relation extraction systems are designed for within-sentence relations, but extracting information from scientific articles requires relations across sentences. |
| Approach: | They propose a multi-task setup for identifying entities, relations, and coreference clusters in scientific articles . they develop a unified framework called SciIE with shared span representations to solve this problem . |
| Outcome: | The proposed model outperforms existing models without domain-specific features in scientific information extraction. |
Explore Unsupervised Structures in Pretrained Models for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Syntactic trees are widely used in relation extraction (RE) but they are not stable on different text domains and a pre-defined grammar may not fit the target relation schema. |
| Approach: | They propose to use unsupervised structures to extract relation extraction models . they also conduct detailed analyses on their abilities of adapting new RE domains . |
| Outcome: | The proposed models obtain competitive (even the best) performance scores on benchmark RE datasets. |
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (2023.emnlp-main)
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| Challenge: | Entity and Relation Extraction (ERE) is an important task in information extraction. |
| Approach: | They propose a hypergraph neural network for ERE built upon the PL-marker . they use a pruner mechanism to transfer the burden of entity identification to the joint module . |
| Outcome: | The proposed model improves on three widely used benchmarks on ERE task . it uses a pruner mechanism to transfer the burden of entity identification to the joint module . |
Unexpected Phenomenon: LLMs’ Spurious Associations in Information Extraction (2024.findings-acl)
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Weiyan Zhang, Wanpeng Lu, Jiacheng Wang, Yating Wang, Lihan Chen, Haiyun Jiang, Jingping Liu, Tong Ruan
| Challenge: | Information extraction (IE) tasks require a limited number of example instructions to achieve effective performance. |
| Approach: | They propose two strategies to find spurious associations in large language models (LLMs) they use forward label extension and backward label validation to leverage extended labels to improve model performance. |
| Outcome: | The proposed methods improve performance on Chinese and English datasets and 9.55%, 11.42%, and 21.27% in F1 scores on SciERC, ACE05, and DuEE datasets. |
ITER: Iterative Transformer-based Entity Recognition and Relation Extraction (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP generate structured information in an autoregressive manner, causing low throughput . authors propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Approach: | They propose an efficient encoder-based relation extraction model that performs the task in three parallelizable steps. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets and is faster than existing models. |
Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)
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| Challenge: | a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities. |
| Approach: | They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction. |
| Outcome: | The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets. |