4 citations · 5 across the 4 of their papers we have counts for
4 papers
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
William Hogan, Jiacheng Li, Jingbo Shang
Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled te…
An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods
William Hogan
Relation Extraction (RE) is a foundational task of natural language processing. RE seeks to transform raw, unstructured text into structured knowledge by identifying relational inf…
Fine-grained Contrastive Learning for Relation Extraction
William Hogan, Jiacheng Li, Jingbo Shang
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on…
Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction
William Hogan, Molly Huang, Yannis Katsis +5
Relation extraction in the biomedical domain is a challenging task due to a lack of labeled data and a long-tail distribution of fact triples. Many works leverage distant supervisi…