Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies

TL;DR

This paper presents a method combining ontologies, description logics, and rule-based linguistic patterns to automatically identify and analyze arguments in breast cancer scientific literature, aiding understanding of medical debates.

Contribution

It introduces a novel argument mining approach that integrates domain ontologies and formal logic to improve argument detection in medical research papers.

Findings

Achieved F-measure between 0.71 and 0.86 for conclusions

Achieved F-measure between 0.65 and 0.86 for premises

Demonstrated effectiveness on breast cancer scientific corpus

Abstract

In the medical domain, the continuous stream of scientific research contains contradictory results supported by arguments and counter-arguments. As medical expertise occurs at different levels, part of the human agents have difficulties to face the huge amount of studies, but also to understand the reasons and pieces of evidences claimed by the proponents and the opponents of the debated topic. To better understand the supporting arguments for new findings related to current state of the art in the medical domain we need tools able to identify arguments in scientific papers. Our work here aims to fill the above technological gap. Quite aware of the difficulty of this task, we embark to this road by relying on the well-known interleaving of domain knowledge with natural language processing. To formalise the existing medical knowledge, we rely on ontologies. To structure the argumentation model we use also the expressivity and reasoning capabilities of Description Logics. To perform argumentation mining we formalise various linguistic patterns in a rule-based language. We tested our solution against a corpus of scientific papers related to breast cancer. The run experiments show a F-measure between 0.71 and 0.86 for identifying conclusions of an argument and between 0.65 and 0.86 for identifying premises of an argument.