{"id":"W4280632712","doi":"10.1162/coli_a_00447","title":"Noun2Verb: Probabilistic Frame Semantics for Word Class Conversion","year":2022,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Linguistics; Verb; Principle of compositionality; Semantics (computer science); Programming language","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":[0.002115241,0.0005150759,0.0005936728,0.000665053,0.0007021105,0.001746831,0.002277117,0.0009649245,0.005434425],"category_scores_gemma":[0.00824252,0.0005531334,0.001310401,0.0005009169,0.001879527,0.003441574,0.001781883,0.001761772,0.0006006217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288134,"about_ca_system_score_gemma":0.001099422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007537564,"about_ca_topic_score_gemma":0.007774788,"domain_scores_codex":[0.9987594,0.0006087006,0.00005231245,0.0002948811,0.0002141902,0.0000706176],"domain_scores_gemma":[0.996763,0.002052961,0.0002555358,0.0005807923,0.0002486709,0.0000989387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003514232,0.0002177726,0.003355871,0.0002317336,0.00011316,0.0003975972,0.001543651,0.3681339,0.01272362,0.4836313,0.006138548,0.1231614],"study_design_scores_gemma":[0.00001814818,0.00002452796,0.000277258,0.000008795659,0.000006968929,0.0000496344,0.00004298327,0.8778821,0.001300054,0.1186825,0.001692625,0.00001446024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02887044,0.00006687344,0.9668657,0.0002901995,0.00004115323,0.0000505561,0.0002421232,0.001560821,0.002012185],"genre_scores_gemma":[0.7524219,0.00008956412,0.2448654,0.0001403714,0.00003687329,0.0002004268,0.0006064351,0.000347126,0.001291972],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007537564,"threshold_uncertainty_score":0.01817995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679350788684265,"score_gpt":0.2792208349685442,"score_spread":0.2624273270817015,"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."}}