Papers with unsupervised relation extraction
Deep Bidirectional Transformers for Relation Extraction without Supervision (D19-61)
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| Challenge: | Existing frameworks for relation extraction use distant supervision instead of annotated data. |
| Approach: | They propose a framework to deal with relation extraction tasks without supervision . they use syntactic parsing and pre-trained word embeddings to extract relations . |
| Outcome: | The proposed framework outperforms baselines on four biomedical datasets and achieves slightly worse results than the state-of-the-art in three out of four data sets. |
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion (2020.findings-emnlp)
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| Challenge: | Existing methods to extract relations from text use fine-tuned machine learning approaches. |
| Approach: | They introduce a framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. |
| Outcome: | The proposed framework outperforms competing unsupervised relation extraction methods based on pretrained language models by 27.8 F1 points compared to the next-best method. |
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction (2022.naacl-main)
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| Challenge: | Existing methods to extract relational feature signals from natural language sentences use self-supervised clustering and classification that cause gradual drift problems. |
| Approach: | They propose a framework that derives hierarchical signals from relational feature space using cross hierarchy attention and effectively optimizes relation representation of sentences under exemplar-wise contrastive learning. |
| Outcome: | The proposed framework can extract the relationship between entities from natural language sentences without prior knowledge on relation scope or distribution. |