Given a query, the query-entity (QE) matching task involves identifying the best matching entity for the query. When modeling this task as a binary classi\ufb01cation problem, two issues arise: (1) features in speci\ufb01c global markets (like de-at: German users in Austria) a requites parse compared to other markets like en-us, and (2) training data is expensive to obtain in multiple markets and hence limited. Can we leverage some form of cross market data\/features for effective query-entity matching in sparse markets? Our solution consists of three main modules: (1) Cross Market Training Data Leverage (CMTDL) (2) Cross Market Feature Leverage (CMFL), and (3) Cross Market Output Data Leverage (CMODL). Each of these parts perform \u201csignal\u201d sharing at different points during the classi\ufb01cation process. Using a combination of these strategies, we show signi\ufb01cant improvements in query-impression weighted coverage for the query-entity matching task.<\/p>\n","protected":false},"excerpt":{"rendered":"
Given a query, the query-entity (QE) matching task involves identifying the best matching entity for the query. When modeling this task as a binary classi\ufb01cation problem, two issues arise: (1) features in speci\ufb01c global markets (like de-at: German users in Austria) a requites parse compared to other markets like en-us, and (2) training data is […]<\/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":"","msr-author-ordering":[{"type":"user_nicename","value":"gmanish"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"Proc. of the 23rd Intl. World Wide Web Conf. 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