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

We propose a novel framework for overcoming the challenges in extracting causal relations from domain-specific texts. Our technique is minimally-supervised, alleviating the need for manually-annotated, expensive training data. As our main contribution, we show that open-domain corpora can be exploited as knowledge bases to overcome data sparsity issues posed by domain-specific relation extraction, and that they enable substantial performance gains. We also address longstanding challenges of extant minimally-supervised approaches. To suppress the negative impact of semantic drift, we propose a technique based on the Latent Relational Hypothesis. In addition, our approach discovers both explicit (e.g. ''to cause'') and implicit (e.g. ''to destroy'') causal patterns/relations. Unlike existing minimally-supervised techniques, we adopt a principled seed selection strategy, which enables us to discover a more diverse set of causal patterns/relations. Our experiments reveal that our approach outperforms a state-of-the-art baseline in discovering causal relations from a real-life, domain-specific corpus.