{"id":"W2046670498","doi":"10.3115/1118771.1118773","title":"An intelligent terminology database as a pre-processor for statistical machine translation","year":2002,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Terminology; Machine translation; Natural language processing; Artificial intelligence; Architecture; Translation (biology); Database; 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":[0.003136948,0.0004887289,0.0009686467,0.003724086,0.0008325129,0.003656765,0.002052093,0.0007928115,0.006229915],"category_scores_gemma":[0.005405449,0.0004874576,0.0007592027,0.004123023,0.0007512086,0.004842665,0.001725752,0.00129698,0.004639416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006694293,"about_ca_system_score_gemma":0.001196968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163451,"about_ca_topic_score_gemma":0.001454367,"domain_scores_codex":[0.9984616,0.0004093059,0.0002379684,0.0002625558,0.0005562914,0.00007223314],"domain_scores_gemma":[0.9965627,0.001183962,0.0001851358,0.0009387994,0.001023932,0.0001055799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008081077,0.0001847141,0.003646956,0.000660671,0.0001625071,0.000572855,0.001078244,0.008673292,0.07059596,0.1026514,0.01767814,0.7932872],"study_design_scores_gemma":[0.0002462541,0.00075752,0.004484369,0.0002654234,0.000685094,0.00293582,0.0013223,0.3032428,0.1880764,0.1639126,0.3338339,0.0002374228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01593986,0.001344127,0.9652542,0.0005708002,0.0002213863,0.0002495628,0.001321928,0.0076723,0.007425887],"genre_scores_gemma":[0.1353045,0.001045831,0.8549255,0.0003715796,0.00020636,0.0002526115,0.003953214,0.0005654439,0.003374893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006229915,"threshold_uncertainty_score":0.02084112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196557607069874,"score_gpt":0.3429050682630908,"score_spread":0.3009394921923921,"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."}}