{"id":"W2052806359","doi":"10.3115/1613715.1613843","title":"Computing word-pair antonymy","year":2008,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Maryland; National Science Foundation","keywords":"Computer science; Word (group theory); Natural language processing; Set (abstract data type); Artificial intelligence; Measure (data warehouse); Linguistics; Data mining; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001239638,0.00008833074,0.00009455095,0.00007163601,0.0001641417,0.00005915292,0.0007992471,0.00004166642,0.00001676505],"category_scores_gemma":[0.00003168654,0.00006987365,0.0000338978,0.0003552861,0.00004075386,0.000371888,0.0003064767,0.000123673,0.00004926987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000020768,"about_ca_system_score_gemma":0.0000378995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003171961,"about_ca_topic_score_gemma":0.000001598013,"domain_scores_codex":[0.9992285,0.00002072694,0.0001228191,0.0002424137,0.0001782968,0.0002072176],"domain_scores_gemma":[0.9994681,0.00004413081,0.00004269593,0.0003364813,0.00005695123,0.00005165],"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":[0.000005136481,0.0001416693,0.009008984,0.00003982291,0.00002115308,0.0006650583,0.003330593,0.0000147991,0.007416348,0.3286743,0.05631004,0.5943721],"study_design_scores_gemma":[0.001114031,0.0002676138,0.009782482,0.0002300222,0.000009199778,0.003081996,0.00007615016,0.2896475,0.5239284,0.1340762,0.03548189,0.002304533],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01610539,0.0007489942,0.9749908,0.0008333598,0.0001081462,0.00005729699,9.465135e-8,0.002415286,0.004740648],"genre_scores_gemma":[0.4751546,0.000002739262,0.5237395,0.0005034342,0.00003077142,6.039936e-7,2.370327e-7,0.000003227804,0.0005648743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5920675,"threshold_uncertainty_score":0.2849365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752822205499096,"score_gpt":0.2649394851317067,"score_spread":0.2474112630767158,"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."}}