Selected Approaches Ranking Contextual Term for the BioASQ Multi-label Classification (Task6a and 7a)

Abstract

MeSH annotations are attached to the Medline abstracts to improve retrieval and this service is provided from the curators at the National Library of Medicine (NLM). Efforts to automatically assign such headings to Medline abstracts have proven difficult, on the other side, such approaches would increase throughput and efficiency. Trained solutions, i.e. machine learning solutions, achieve promising results, however these advancements do not fully explain, which features from the text would suit best the identification of MeSH Headings from the abstracts. This manuscript describes new approaches for the identification of contextual features for automatic MeSH annotations, which is a Multi-Label Classification (BioASQ Task6a): more specifically, different approaches for the identification of compound terms have been tested and evaluated. The described system has then been extended to better rank selected labels and has been tested in the BioASQ Task7a challenge. The tests show that our recall measures (see Task6a) have improved and in the second challenge, both the performance for precision and recall were boosted. Our work improves our understanding how contextual features from the text help reduce the performance gap given between purely trained solutions and feature-based solutions (possibly including trained solutions). In addition, we have to point out that the lexical features given from the MeSH thesaurus come with a significant and high discrepancy towards the actual annotations of MeSH Headings attributed by human curators, which also hinders improvements to the automatic annotation of Medline abstracts with MeSH Headings.