Siamese Representation Learning for Unsupervised Relation Extraction
Guangxin Zhang Shu Chen
Unsupervised relation extraction (URE) aims at discovering underlying
relations between named entity pairs from open-domain plain text without prior
information on relational distribution. Existing URE models utilizing
contrastive learning, which attract positive samples and repulse negative
samples to promote better separation, have got decent effect. However,
fine-grained relational semantic in relationship makes spurious negative
samples, damaging the inherent hierarchical structure and hindering
performances. To tackle this problem, we propose Siamese Representation
Learning for Unsupervised Relation Extraction -- a novel framework to simply
leverage positive pairs to representation learning, possessing the capability
to effectively optimize relation representation of instances and retain
hierarchical information in relational feature space. Experimental results show
that our model significantly advances the state-of-the-art results on two
benchmark datasets and detailed analyses demonstrate the effectiveness and
robustness of our proposed model on unsupervised relation extraction.