In this paper we address the problem of identifying a broad range of term variations in Japanese web search queries, where these variations pose a particularly thorny problem due to the multiple character types employed in its writing system. Our method extends the techniques proposed for English spelling correction of web queries to handle a wider range of term variants including spelling mistakes, valid alternative spellings using multiple character types, transliterations and abbreviations. The core of our method is a statistical model built on the MART algorithm (Friedman, 2001). We show that both string and semantic similarity features contribute to identifying term variation in web search queries; specifically, the semantic similarity features used in our system are learned by mining user session and click-through logs, and are useful not only as model features but also in generating term variation candidates efficiently. The proposed method achieves 70% precision on the term variation identification task with the recall slightly higher than 60%, reducing the error rate of a naïve baseline by 38%.