Semi-supervised Relation Extraction with Large-scale Word Clustering

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Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, methodology): “, 2014; Miwa and Bansal, 2016), semi-supervised (Chen et al., 2006; Sun et al., 2011; Hu et al., 2020) and distantly supervised methods (Mintz et al.”
Improved Neural Relation Detection for Knowledge Base Question Answering (2017, methodology): “Traditional RE methods rely on large amount of hand-crafted features (Zhou et al., 2005; Rink and Harabagiu, 2010; Sun et al., 2011).”
Semi-Supervised Learning to Identify UMLS Semantic Relations (2014, background): “Recently, semi-supervised or unsupervised acquisition of semantic relations has gained traction in the general NLP domain, where the methods typically include clustering and co-clustering algorithms that are often augmented with seeding or subsequent supervised classification [14][15][16].”
Combining Neural Networks and Log-linear Models to Improve Relation Extraction (2015, methodology): “…relation mention for neural networks, we ﬁrst transform each word w i into a real-valued vector x i using the concatenation of the following seven vectors, motivated by the previous research on neural networks and feature analysis for RE (Zhou et al., 2005; Sun et al., 2011; Gormley et al., 2015).”