Query Topic Classification Based on Wikipedia
Xiaolian Liu · Information Sciences · 2014
A query topic classifier with two-stage based on Wikipedia is analyzed and designed. The theme-related semantic features with query words are acquired by query expansion, and query are labeled by semantic relatedness when user queries and classification labels are transferred and bridged to wikipedia directories. A design and realization in practical application are provided. The experiment results show that the proposed method can be used to solve the data sparseness problem, and performs well on precision rate and recall rate in query classification.