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Found 3 papers, 1 papers with code
Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings
We propose REDSandT (Relation Extraction with Distant Supervision and Transformers), a novel distantly-supervised transformer-based RE method, that manages to capture a wider set of relations through highly informative instance and label embeddings for RE, by exploiting BERT's pre-trained model, and the relationship between labels and entities, respectively.
Ranked #2 on
Relationship Extraction (Distant Supervised)
on New York Times Corpus
(AUC metric)
Improving Zero-Shot Entity Retrieval through Effective Dense Representations
We achieve a new state-of-the-art 84. 28% accuracy on top-50 candidates on the Zeshel dataset, compared to the previous 82. 06% on the top-64 of (Wu et al., 2020).
The concept of nation in nineteenth-century Greek fiction through computational literary analysis
How the construction of national consciousness may be captured in the literary production of a whole century?
