  We propose a knowledge-based approach for extraction of Cause-Effect (CE)
relations from biomedical text. Our approach is a combination of an
unsupervised machine learning technique to discover causal triggers and a set
of high-precision linguistic rules to identify cause/effect arguments of these
causal triggers. We evaluate our approach using a corpus of 58,761
Leukaemia-related PubMed abstracts consisting of 568,528 sentences. We could
extract 152,655 CE triplets from this corpus where each triplet consists of a
cause phrase, an effect phrase and a causal trigger. As compared to the
existing knowledge base - SemMedDB (Kilicoglu et al., 2012), the number of
extractions are almost twice. Moreover, the proposed approach outperformed the
existing technique SemRep (Rindflesch and Fiszman, 2003) on a dataset of 500
sentences.
