{"id":157945,"date":"2009-08-01T00:00:00","date_gmt":"2009-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/discovery-of-term-variation-in-japanese-web-search-queries\/"},"modified":"2018-10-16T20:07:17","modified_gmt":"2018-10-17T03:07:17","slug":"discovery-of-term-variation-in-japanese-web-search-queries","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/discovery-of-term-variation-in-japanese-web-search-queries\/","title":{"rendered":"Discovery of Term Variation in Japanese Web Search Queries"},"content":{"rendered":"
\n

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 na\u00efve baseline by 38%.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-157945","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"Association for Computational Linguistics","msr_edition":"Proceedings of 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