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Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text
[article]
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
arXiv:2103.06078v1
fatcat:vjrrati5wfbytlop4h27hbldwa