{"id":"W2107295905","doi":"10.7202/004014ar","title":"Lexical Cohesion and Translation Equivalence","year":2002,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cohesion (chemistry); Linguistics; Equivalence (formal languages); Computer science; Lexical functional grammar; Sentence; Lexical choice; Natural language processing; Lexical item; Co-occurrence; Lexical semantics; Artificial intelligence; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002267539,0.0004137673,0.0005986896,0.00282687,0.002351331,0.00444219,0.0006813246,0.001196629,0.008826919],"category_scores_gemma":[0.01582359,0.0002745122,0.0005511653,0.001834615,0.006008022,0.008199248,0.004452079,0.001225604,0.001222874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090306,"about_ca_system_score_gemma":0.0006711279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009389222,"about_ca_topic_score_gemma":0.0005000844,"domain_scores_codex":[0.9952873,0.002096136,0.0004981385,0.0009856266,0.0007701071,0.0003627017],"domain_scores_gemma":[0.9937337,0.003332593,0.0007770087,0.0009312992,0.001011434,0.0002139644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008319204,0.00005034624,0.001675311,0.0001667917,0.0000306221,0.0004640405,0.008119142,0.0006616169,0.002413967,0.9243416,0.002116793,0.05987666],"study_design_scores_gemma":[0.000035949,0.00008390091,0.003200687,0.00007849296,0.00002829789,0.0003976201,0.002419193,0.001882412,0.001358156,0.9647235,0.02576894,0.00002276646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2901742,0.004117745,0.2954217,0.006042122,0.0008402827,0.0004247442,0.0006033571,0.0007382587,0.4016375],"genre_scores_gemma":[0.9629275,0.000750127,0.02400233,0.0002967858,0.0004298609,0.0002347639,0.000445149,0.0001454139,0.01076813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008826919,"threshold_uncertainty_score":0.02952898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07181585355392639,"score_gpt":0.2881342644734426,"score_spread":0.2163184109195163,"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."}}