KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge

This paper describes a question– answering system that returns relevant documents and snippets (with particular emphasis on snippets) from a large medical document collection. The system is implemented as part of our participation to Phase A of Task 4b in the 2016 BioASQ Challenge. The proposed system retrieves candidate answer sentences using a cluster–based language model. Then, it re–ranks the retrieved top-n sentences using five independent similarity models based on shallow semantic analysis. The experimental results show that the proposed system is the first to find snippets in batches 2 (MAP 0.0604), 3 (MAP 0.0728), 4 (MAP 0.1182), and 5 (MAP 0.1187).