Knowledge-based Extraction of Cause–Effect Relations from Biomedical Text

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 [5]—the number of extractions are almost twice. Moreover, the proposed approach outperformed the existing technique SemRep [7] on a dataset of 500 sentences.