Research on semi-supervised Chinese relation type discovery

In this paper, we propose a novel semi-supervised model to discover those missing relation types in labeled corpus and fulfill the aim of relation extraction automatically. We combine language information and structured information to represent candidate relation instances. First, we make use of Bootstrapping and Label Propagation algorithms to label the relation instances, whose types have existed in corpus. Second, we use unsupervised method to cluster the remaining relation instances and discover the missing relation types. Evaluation on the ACE2005 corpus shows that our proposed method can achieve ideal experimental results.