The clustering-based unsupervised relation discovery method has gradually
become one of the important methods of open relation extraction (OpenRE).
However, high-dimensional vectors can encode complex linguistic information
which leads to the problem that the derived clusters cannot explicitly align
with the relational semantic classes. In this work, we propose a
relation-oriented clustering model and use it to identify the novel relations
in the unlabeled data. Specifically, to enable the model to learn to cluster
relational data, our method leverages the readily available labeled data of
pre-defined relations to learn a relation-oriented representation. We minimize
distance between the instance with same relation by gathering the instances
towards their corresponding relation centroids to form a cluster structure, so
that the learned representation is cluster-friendly. To reduce the clustering
bias on predefined classes, we optimize the model by minimizing a joint
objective on both labeled and unlabeled data. Experimental results show that
our method reduces the error rate by 29.2% and 15.7%, on two datasets
respectively, compared with current SOTA methods.

本文提出一种关注于关系的聚类方法，使用已标注的关系数据训练模型，使聚类结果更符合关系的语义类别，旨在提高开放关系提取的准确性和效率。