Classifying Biological Full-Text Articles for Multi-Database Curation

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 ract, 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.

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Wen-Juan Hou, Chih Lee, Hsin-Hsi Chen
• Keywords: Domain-specific Resources, EACL 2006, Individual Representations, Natural Language Processing, SVM Classifier