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Statistical ranking methods based on centroid vector (profile) extracted from external knowledge have become widely adopted in the top definitional QA systems in TREC 2003 and 2004. In these approaches, terms in the centroid vector are treated as a bag of words based on the independent assumption. To relax this assumption, this paper proposes a novel language model-based answer reranking method to improve the existing bag-ofwords model approach by considering the dependence of the words in thedoi:10.3115/1220175.1220311 dblp:conf/acl/ChenZW06 fatcat:7if62jfzkbbkjfjprnz5at2vxm