CUIS Team for NTCIR-13 AKG Task

This paper describes our approach for Actionable Knowledge Graph (AKG) task at NTCIR-13. Our ranking system scores each candidate property by combining semantic relevance to action and its document relevance in related entity text descriptions via a Dirichlet smoothing based language model. We employ supervised learning technique to improve performance by minimizing a simple position-sensitive loss function on our additional manually annotated training data from the dry run topics. Our best submission achieves NDCG@10 of 0.5753 and NDCG@20 of 0.7358 in the Actionable Knowledge Graph Generation (AKGG) subtask.