Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text
This work addresses the challenge of automatically identifying causal relationships in biomedical literature, which is incremental as it builds upon existing methods with improved extraction rates.
The authors tackled the problem of extracting cause-effect relations from biomedical text by proposing a knowledge-based approach combining unsupervised machine learning and linguistic rules, resulting in 152,655 CE triplets extracted from a corpus of 58,761 abstracts, which is almost twice the number compared to an existing knowledge base.
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.