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Found 5 papers, 3 papers with code
Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
This paper delves into Named Entity Recognition (NER) under the framework of Distant Supervision (DS-NER), where the main challenge lies in the compromised quality of labels due to inherent errors such as false positives, false negatives, and positive type errors.
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs.
Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion
Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample.
Improving Distantly Supervised Relation Extraction by Natural Language Inference
To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance.
Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning
In this paper, we study the named entity recognition (NER) problem under distant supervision.
