Papers with relation extraction model
Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism (D19-66)
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| Challenge: | Existing studies on relation extrac-tion focus on finding only one relation between two entities in a single sentence. |
| Approach: | They propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism that finds n-to-1 subject-object relations by using a forward decoder and a backward decode-r. |
| Outcome: | The proposed model achieves the state-of-the-art performance on the ACE-05 and NYT datasets. |
Unsupervised Relation Extraction: A Variational Autoencoder Approach (2021.emnlp-main)
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| Challenge: | Existing methods for relation extraction use latent variables and supervised training which requires large datasets. |
| Approach: | They propose a VAE-based unsupervised relation extraction technique that uses latent variables as an intermediate variable instead of a latent variable. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the NYT dataset and outperformed existing methods. |
BIOPSY - Biomarkers In Oncology: Pipeline for Structured Yielding (2025.emnlp-industry)
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| Challenge: | Biomarkers are crucial indicators for early cancer detection and prognosis, but extracting biomarkers from clinical texts remains a challenge. |
| Approach: | They propose a pipeline that integrates a domain-adapted biomarker entity recognition model and a relation extraction model to link biomarkers to their respective mutations. |
| Outcome: | The proposed pipeline achieves an F1 score of 0.86 for oncology and 0.87 for neuroscience domains on 5,000 clinical texts. |
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (N19-1)
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| Challenge: | Existing distance supervised relation extraction models for long-tail data are inadequate for many applications. |
| Approach: | They propose to leverage implicit relational knowledge among class labels and learn explicit relational knowing using graph convolution networks. |
| Outcome: | The proposed approach outperforms baselines for long-tail relations on a large-scale dataset. |