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

  • Authors:

  • Ashwin Ittoo;Gosse Bouma

  • Affiliations:

  • Operations, Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747 AE Groningen, The Netherlands;Computational Linguistics (Information Science), Faculty of Arts, University of Groningen, Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2013

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Abstract

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.