A generic retrieval system for biomedical literatures: USTB at BioASQ2015 Question Answering Task
Zhijuan Zhang, Tiantian Liu, Bowen Zhang, Li Yan, Chun Hua Zhao, Shao-Hui Feng, Xu-Cheng Yin, Fang Zhou · CLEF (Working Notes) · 2015
In this paper we describe our participation in the 2015 BioASQ challenge task on question answering (Phase A). Participants need to re- spond to the natural language questions in the format of documents, snippets, concepts and RDF triplets. In document retrieval, we build a generic retrieval model based on the sequential dependence model, Word Embedding and Ranking Model. In addition, from the view of the special signicance of titles(Title Signicance Validation), we re-rank the top-K results by counting the meaningful nouns in the titles. The top-K doc- uments are split into sentences and indexed for snippets retrieval. The similar models of document retrieval are applied for this part. To extract the biomedical concepts and corresponding RDF triplets, we use concept recognition tools MetaMap and Banner 1 . Statistics indicate that our