Japanese query alteration based on semantic similarity
Masato Hagiwara, Hisami Suzuki · 2009
We propose a unified approach to web search query alterations in Japanese that is not limited to particular character types or orthographic similarity between a query and its alteration candidate. Our model is based on previous work on English query correction, but makes some crucial improvements: (1) we augment the query-candidate list to include orthographically dissimilar but semantically similar pairs; and (2) we use kernel-based lexical semantic similarity to avoid the problem of data sparseness in computing query-candidate similarity. We also propose an efficient method for generating query-candidate pairs for model training and testing. We show that the proposed method achieves about 80% accuracy on the query alteration task, improving over previously proposed methods that use semantic similarity.