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• W2149706974 abstract "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 naive baseline by 38%." @default.
• W2149706974 created "2016-06-24" @default.
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• W2149706974 date "2009-01-01" @default.
• W2149706974 modified "2023-10-18" @default.
• W2149706974 title "Discovery of term variation in Japanese web search queries" @default.
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• W2149706974 doi "https://doi.org/10.3115/1699648.1699695" @default.
• W2149706974 hasPublicationYear "2009" @default.
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