Ramakanth Kavuluru, and Anthony Rios.
In proceedings of the 2015 AMIA Annual Symposium.
Assigning labels from a hierarchical vocabulary is a well known special case of multi-label classification, often modeled to maximize micro F1-score. However, building accurate binary classifiers for poorly performing labels in the hierarchy can improve both micro and macro F1-scores. In this paper, we propose and evaluate classification strategies involving descendant node instances to build better binary classifiers for non-leaf labels with the use-case of assigning Medical Subject Headings (MeSH) to biomedical articles. Librarians at the National Library of Medicine tag each biomedical article to be indexed by their PubMed information system with terms from the MeSH terminology, a biomedical conceptual hierarchy with over 27,000 terms. Human indexers look at each article’s full text to assign a set of most suitable MeSH terms for indexing it. Several recent automated attempts focused on using the article title and abstract text to identify MeSH terms for the corresponding article. Despite these attempts, it is observed that assigning MeSH terms corresponding to certain non-leaf nodes of the MeSH hierarchy is particularly challenging. Non-leaf nodes are very important as they constitute one third of the total number of MeSH terms. Here, we demonstrate the effectiveness of exploiting training examples of descendant terms of non-leaf nodes in improving the performance of conventional classifiers for the corresponding non-leaf MeSH terms. Specifically, we focus on reducing the false positives (FPs) caused due to descendant instances in traditional classifiers. Our methods are able to achieve a relative improvement of 7.5% in macro-F1 score while also increasing the micro-F1 score by 1.6% for a set of 500 non-leaf terms in the MeSH hierarchy. These results strongly indicate the critical role of incorporating hierarchical information in MeSH term prediction. To our knowledge, our effort is the first to demonstrate the role of hierarchical information in improving binary classifiers for non-leaf MeSH terms.