Mining Named Entities from Query Logs
Hongbo Xu · Zhongwen xinxi xuebao · 2010
Mining named entities from query logs is an important research field in data mining.Previous work proposed a seed-based framework to mine named entities from query logs by leveraging distribution similarity,which works well only when each named entity only belongs to a signle semantic class.In fact,named entities may often belong to multiple classes.In this paper,we introduce a weakly-supervised topic model to resolve class ambiguity of named entities by leveraging weak supervision from human.The experiment results show that our approach significantly outperforms the previous method.