Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text [article]

Sachin Pawar, Ravina More, Girish K. Palshikar, Pushpak Bhattacharyya, Vasudeva Varma
2021 arXiv   pre-print
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
more » ... riplet 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.
arXiv:2103.06078v1 fatcat:vjrrati5wfbytlop4h27hbldwa