{"id":"W2046353967","doi":"10.1145/1740592.1740594","title":"Exploiting query logs for cross-lingual query suggestions","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Microsoft Research Asia; Chinese University of Hong Kong; Innovation and Technology Commission","keywords":"Computer science; Cross-language information retrieval; Query expansion; Query language; RDF query language; Query optimization; Information retrieval; Web query classification; Sargable; Natural language processing; Relevance (law); Web search query; Discriminative model; Query by Example; Artificial intelligence; Search engine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007814266,0.002422083,0.002437064,0.00551097,0.001246027,0.003119016,0.002249177,0.001326312,0.003412267],"category_scores_gemma":[0.04233227,0.001269802,0.0009190781,0.004314958,0.0006778713,0.0091549,0.002585227,0.002520194,0.003177269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083285,"about_ca_system_score_gemma":0.003100764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008908823,"about_ca_topic_score_gemma":0.01276348,"domain_scores_codex":[0.9915353,0.003829512,0.0007176275,0.001073354,0.002516988,0.0003272876],"domain_scores_gemma":[0.9575999,0.02735853,0.002222779,0.005802633,0.006365906,0.000650103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003982096,0.002089229,0.03015747,0.002504461,0.0004433674,0.001118262,0.00296739,0.03659524,0.07507897,0.006487144,0.04029147,0.7982849],"study_design_scores_gemma":[0.0002496988,0.0007000424,0.008089249,0.0001298465,0.0002743356,0.0007312777,0.001102986,0.9113534,0.03740725,0.01285029,0.02686367,0.0002479376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1567411,0.005383803,0.7637402,0.002569324,0.0004349156,0.001648606,0.006959432,0.05466308,0.007859611],"genre_scores_gemma":[0.6988245,0.001316275,0.2796897,0.0005582467,0.0002844184,0.0007373479,0.01404626,0.001414001,0.003129356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008908823,"threshold_uncertainty_score":0.04132634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02778635592133692,"score_gpt":0.2897494147459037,"score_spread":0.2619630588245668,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}