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

TL;DR

This paper presents a knowledge-based method combining machine learning and linguistic rules to extract cause-effect relations from biomedical texts, significantly increasing extraction coverage and outperforming existing tools.

Contribution

It introduces a novel hybrid approach that improves the extraction of cause-effect relations from biomedical literature over previous methods.

Findings

Extracted 152,655 CE triplets from Leukaemia abstracts

Nearly doubled the number of relations compared to SemMedDB

Outperformed SemRep on a benchmark dataset

Abstract

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.