{"id":"W178463821","doi":"","title":"Improving Query Translation for CLIR Using Statistical Models","year":2017,"lang":"en","type":"article","venue":"International ACM SIGIR Conference on Research and Development in Information Retrieval","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Machine translation; Cross-language information retrieval; Translation (biology); Principle of maximum entropy; Cohesion (chemistry); Word (group theory); Phrase; Query expansion; Rule-based machine translation; Information retrieval; Linguistics","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":[0.004340593,0.001827239,0.002057944,0.003065519,0.0009881081,0.001827111,0.001843839,0.00137834,0.004056699],"category_scores_gemma":[0.01367289,0.0007574164,0.001877591,0.004433515,0.0007797951,0.003718453,0.001455295,0.001778961,0.006440961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245936,"about_ca_system_score_gemma":0.002400898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009847313,"about_ca_topic_score_gemma":0.01275094,"domain_scores_codex":[0.9953832,0.002277506,0.0003605532,0.0007312432,0.001026147,0.0002212595],"domain_scores_gemma":[0.9931166,0.003840061,0.0003503332,0.0009994896,0.001591685,0.000101731],"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":[0.0006263531,0.0006724112,0.002593701,0.001138852,0.0003144224,0.000299567,0.0004745694,0.1344115,0.0503055,0.01622867,0.02598597,0.7669484],"study_design_scores_gemma":[0.00006273175,0.0001662173,0.000414928,0.00001807984,0.00006926767,0.0001788383,0.00009881918,0.9768487,0.009801519,0.00717249,0.005126138,0.00004238519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01353519,0.001792348,0.9746383,0.0004733885,0.0001503384,0.000219955,0.0003729035,0.006886255,0.001931299],"genre_scores_gemma":[0.2355515,0.001963333,0.752973,0.0008003571,0.0003899331,0.0005440926,0.002814247,0.001104182,0.0038594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009847313,"threshold_uncertainty_score":0.02295548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2123779867351904,"score_gpt":0.4237923905713148,"score_spread":0.2114144038361244,"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."}}