Classifying biological full-text articles for multi-database curation

Abstract

In this paper, we propose an approach for identifying curatable articles from a large document set. This system considers three parts of an article (title and abstract, MeSH terms, and captions) as its three individual representations and utilizes two domain-specific resources (UMLS and a tumor name list) to reveal the deep knowledge contained in the article. An SVM classifier is trained and cross-validation is employed to find the best combination of representations. The experimental results show overall high performance.