{"id":"W2052801201","doi":"10.1075/ml.2.3.05ji","title":"Lexical and relational influences on the processing of Chinese modifier-noun compounds","year":2007,"lang":"en","type":"article","venue":"The Mental Lexicon","topic":"EFL/ESL Teaching and Learning","field":"Arts and Humanities","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Noun; Computer science; Natural language processing; Relation (database); Compound; Representation (politics); Linguistics; Artificial intelligence; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0007217744,0.00008268015,0.00008170831,0.00002876817,0.0007287969,0.00007727528,0.00009958112,0.00002027849,0.0001352784],"category_scores_gemma":[0.00002977389,0.00003930142,0.00002958262,0.00002286893,0.0004307873,0.00009608917,0.00002913186,0.0002376032,0.00001075209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001493781,"about_ca_system_score_gemma":0.00001134659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001247533,"about_ca_topic_score_gemma":0.00009206137,"domain_scores_codex":[0.9993873,0.0000626436,0.0001511226,0.00008905706,0.000197529,0.0001124103],"domain_scores_gemma":[0.9995425,0.0002584532,0.00008264608,0.00007952566,0.00001707924,0.00001982923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001450661,0.0001046674,0.02492167,0.00002678566,0.00003280112,0.000001201114,0.1334607,0.00005816036,0.001557356,0.8326072,0.0003043403,0.006780069],"study_design_scores_gemma":[0.003140812,0.001770384,0.4771302,0.001047721,0.0001291321,0.00006457744,0.1183143,0.01231364,0.002762236,0.1653673,0.2165946,0.001365072],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9181386,0.0001822603,0.00001799594,0.001396953,0.00009038706,0.00007213736,0.000003214926,0.00002348541,0.08007502],"genre_scores_gemma":[0.9978685,0.000002387929,0.0000252442,0.0004097329,0.0002319017,0.000002058048,0.000005013284,0.000006797462,0.001448338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6672399,"threshold_uncertainty_score":0.560539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04872314544611183,"score_gpt":0.2857866367114783,"score_spread":0.2370634912653665,"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."}}