{"id":161804,"date":"2011-04-01T00:00:00","date_gmt":"2011-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/fractional-similarity-cross-lingual-feature-selection-for-search\/"},"modified":"2018-10-16T19:59:20","modified_gmt":"2018-10-17T02:59:20","slug":"fractional-similarity-cross-lingual-feature-selection-for-search","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fractional-similarity-cross-lingual-feature-selection-for-search\/","title":{"rendered":"Fractional Similarity: Cross-lingual Feature Selection for Search"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Training data as well as supplementary data such as usage-based click behavior may abound in one search market (i.e., a particular region, domain, or language) and be much scarcer in another market. Transfer methods attempt to improve performance in these resource-scarce markets by leveraging data across markets. However, differences in feature distributions across markets can change the optimal model.We introduce a method called Fractional Similarity, which uses query-based variance within a market to obtain more reliable estimates of feature deviations across markets. An empirical analysis demonstrates that using this scoring method as a feature selection criterion in cross-lingual transfer improves relevance ranking in the foreign language and compares favorably to a baseline based on KL divergence.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Training data as well as supplementary data such as usage-based click behavior may abound in one search market (i.e., a particular region, domain, or language) and be much scarcer in another market. Transfer methods attempt to improve performance in these resource-scarce markets by leveraging data across markets. However, differences in feature distributions across markets can [&hellip;]<\/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":[13555],"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-161804","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 33rd Annual European Conference on Information Retrieval (ECIR 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