Minimally-supervised learning of domain-specific causal relations using an open-domain corpus as knowledge base

Highlights：

• Novel technique that accurately extracts implicit and explicit causal patterns from sparse, ungrammatical domain-specific texts

• Approach is minimally-supervised, eschewing the need for expensive annotated data.

• Exploits open-domain knowledge base to support domain-specific term extraction

• Addresses the issue of semantic drift using Latent Relational Hypothesis

• The proposed technique outperforms a state-of-the-art baseline over real-life texts.