Relation Extraction Using Label Propagation Based Semi-Supervised Learning
By Jinxiu Chen, Donghong Ji, Chew Lim Tan, Zheng-Yu Niu. 2006
114 citations recorded by the source corpus.
Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label propagation (LP) algorithm, for relation extraction. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be fixed on the labeled nodes, 2) it should be smooth on the whole graph. Experiment results on the ACE corpus showed that this LP algorithm achieves better performance than SVM when only very few labeled examples are available, and it also performs better than bootstrapping for the relation extraction task.
Subfields: Artificial Intelligence, Information Systems
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Citations in the corpus
- Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, background): “, 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.”
- Curriculum-Meta Learning for Order-Robust Continual Relation Extraction (2021, methodology): “2016; Miwa and Bansal 2016), semi-supervised methods (Chen et al. 2006; Sun, Grishman, and Sekine 2011; Lin et al. 2019), and distantly supervised methods (Yao et al.”
- Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction (2009, background): “Chen et al. (2006) explored semi-supervised learning for relation extraction using label propagation, which makes use of unlabeled data.”