This before That: Causal Precedence in the Biomedical Domain

TL;DR

This paper introduces a new annotated corpus and evaluates various models to identify causal precedence in biomedical texts, highlighting the effectiveness of combined model architectures for improved accuracy.

Contribution

It presents a novel hand-annotated corpus for causal precedence in biomedicine and demonstrates that combining feature-based and latent models enhances prediction performance.

Findings

Highest individual model F1 score of 43

Feature-based and latent models outperform rule-based models

Sieve architecture improves F1 score to 46

Abstract

Causal precedence between biochemical interactions is crucial in the biomedical domain, because it transforms collections of individual interactions, e.g., bindings and phosphorylations, into the causal mechanisms needed to inform meaningful search and inference. Here, we analyze causal precedence in the biomedical domain as distinct from open-domain, temporal precedence. First, we describe a novel, hand-annotated text corpus of causal precedence in the biomedical domain. Second, we use this corpus to investigate a battery of models of precedence, covering rule-based, feature-based, and latent representation models. The highest-performing individual model achieved a micro F1 of 43 points, approaching the best performers on the simpler temporal-only precedence tasks. Feature-based and latent representation models each outperform the rule-based models, but their performance is complementary to one another. We apply a sieve-based architecture to capitalize on this lack of overlap, achieving a micro F1 score of 46 points.