{"id":"W1522829138","doi":"10.5539/ells.v5n2p18","title":"Lost in Collocation: When Arabic Collocation Dictionaries Lack Collocations","year":2015,"lang":"en","type":"article","venue":"English Language and Literature Studies","topic":"Lexicography and Language Studies","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Collocation (remote sensing); Arabic; Computer science; Linguistics; Natural language processing; Range (aeronautics); Artificial intelligence; English language; Word (group theory); 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":[0.01125706,0.0007531016,0.0008541226,0.005395992,0.006269719,0.008209418,0.001601681,0.001883805,0.0138862],"category_scores_gemma":[0.06678317,0.0007404083,0.0003284837,0.009288492,0.008414843,0.01701648,0.007610356,0.00350654,0.006070323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00288098,"about_ca_system_score_gemma":0.003708031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008556899,"about_ca_topic_score_gemma":0.007898188,"domain_scores_codex":[0.9874796,0.005114033,0.002011315,0.001238276,0.003317068,0.0008397658],"domain_scores_gemma":[0.9420476,0.02142146,0.007440065,0.009829827,0.01713973,0.002121315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000737013,0.0001174594,0.04863753,0.001907629,0.0001118514,0.009510074,0.2165075,0.0003875903,0.008064162,0.196687,0.1456227,0.3717095],"study_design_scores_gemma":[0.00004332809,0.00009927656,0.02172687,0.002080499,0.00007742675,0.01452878,0.1305489,0.00184487,0.004672119,0.06318601,0.7610318,0.0001601221],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.412773,0.02499592,0.1468617,0.1288882,0.01174276,0.0004850257,0.002011439,0.002834823,0.2694073],"genre_scores_gemma":[0.9064535,0.004681095,0.04378557,0.01069666,0.001270122,0.0001748826,0.001264708,0.001268722,0.03040472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0138862,"threshold_uncertainty_score":0.05953377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03809097001244209,"score_gpt":0.2769360101339898,"score_spread":0.2388450401215477,"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."}}