Growing Related Words from Seed via User Behaviors: A Re-Ranking Based Approach
Yabin Zheng, Zhiyuan Liu, Lixing Xie · 2010
Motivated by Google Sets, we study the prob-lem of growing related words from a single seed word by leveraging user behaviors hiding in user records of Chinese input method. Our proposed method is motivated by the observa-tion that the more frequently two words co-occur in user records, the more related they are. First, we utilize user behaviors to generate candidate words. Then, we utilize search en-gine to enrich candidate words with adequate semantic features. Finally, we reorder candi-date words according to their semantic rela-tedness to the seed word. Experimental results on a Chinese input method dataset show that our method gains better performance.