Artemis Dampa
Abstract
Effectively organizing the vast and evergrowing body of research in scientific literature is crucial to advancing the field and supporting
scholarly discovery. In this paper, we study the task of fine-grained hierarchical multi-label classification of scholarly articles, using a structured taxonomy. Specifically, we investigate whether incorporating hierarchical information in a classification method can improve performance compared to conventional flat classification approaches. To this end, we suggest and evaluate different strategies for the classification, on three different axes: selection
of positive and negative samples; soft-to-hard label mapping; hierarchical post-processing policies that utilize taxonomy-related requirements to update the final labeling. Experiments demonstrate that flat baselines constitute powerful baselines, but the infusion of hierarchical knowledge leads to better recall-focused performance based on use-case requirements.