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This paper presents a novel approach to address the Entity Recognition andLinking Challenge at NLPCC 2015. The task involves extracting named entitymentions from short search queries and linking them to entities within areference Chinese knowledge base. To tackle this problem, we first expand theexisting knowledge base and utilize external knowledge to identify candidateentities, thereby improving the recall rate. Next, we extract features from thecandidate entities and utilize Support Vector Regression and Multiple AdditiveRegression Tree as scoring functions to filter the results. Additionally, weapply rules to further refine the results and enhance precision. Our method iscomputationally efficient and achieves an F1 score of 0.535.

AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking