Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies
This addresses the challenge for medical professionals and researchers in managing contradictory evidence from vast scientific literature, though it is incremental as it builds on existing NLP and ontology methods.
The paper tackled the problem of identifying arguments in scientific cancer documents by developing a tool that combines natural language processing with ontologies and description logics, achieving F-measure scores of 0.71-0.86 for conclusions and 0.65-0.86 for premises on a breast cancer corpus.
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.