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

arXiv: https://arxiv.org/abs/2103.06078 · HTML: https://mlsanity.com/paper/2103.06078/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Investigating Counterclaims in Causality Extraction from Text](https://mlsanity.com/paper/2510.08224/) (2025)

## Abstract (arXiv)

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.