Bipartite graph based semi-supervised method for entity mining from the query log
Xueqi Cheng · Journal of Shandong University · 2012
Named entity mining from query log aims to mine a list of named entities with the specific type from the query log.A bipartite graph based semi-supervised ranking method,which leverages the relationship between the entities(i.e.entities share common templates) to help improve the ranking,was proposed to resolve the scarcity of seed entity in existing work about named entity mining from the query log.First,a bipartite graph based on the candidate entities and templates was constructed.Then,the relevance score was propagated from the seed entities to other candidate entities.Finally,the candidate entities were ranked according to the relevance score.An optimization framework for the iterative process was further developed in this ranking method.Experimental results show the effectiveness of the proposed method.