{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001267362,0.0001093361,0.0001121208,0.0004596541,0.000422927,0.001522231,0.00126781,0.00009168497,0.000009891231],"category_scores_gemma":[0.001786574,0.0001002242,0.00001388263,0.00008343941,0.00009927656,0.003709647,0.0003863707,0.0002795249,0.000006513725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806215,"about_ca_system_score_gemma":0.0004824715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006809755,"about_ca_topic_score_gemma":0.0000126705,"domain_scores_codex":[0.9983214,0.00003196346,0.0003858336,0.0002182085,0.0007659512,0.0002766256],"domain_scores_gemma":[0.9982492,0.0004348274,0.0001677497,0.0003074256,0.000760655,0.00008011882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003556982,0.0000483496,0.0004479226,0.00007989637,0.0000136495,0.000005353627,0.001304054,0.00004696976,0.0008217345,0.550747,0.0000899181,0.4460394],"study_design_scores_gemma":[0.000946858,0.0001272572,0.0009957432,0.0001730569,7.265318e-7,0.000007062625,0.00009045663,0.7501355,0.009708464,0.2365458,0.001034418,0.0002345655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0138537,0.00002362639,0.9832892,0.001204354,0.0002004614,0.0003645511,0.00001969053,0.00006183622,0.0009826006],"genre_scores_gemma":[0.5264164,0.00001441914,0.4734186,0.00005727267,0.0000189301,0.00002111135,0.00003248507,0.000002826991,0.00001797709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7500886,"threshold_uncertainty_score":0.9995143,"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."}}