{"id":"W2169943035","doi":"10.48550/arxiv.1302.4813","title":"Probabilistic Frame Induction","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Merge (version control); Parsing; Probabilistic logic; Artificial intelligence; Frame (networking); Natural language processing; Natural language; Event (particle physics); Set (abstract data type); Information retrieval; 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":[0.002306284,0.001584043,0.001067489,0.002966651,0.00155578,0.001344387,0.003256156,0.001593893,0.009481852],"category_scores_gemma":[0.008812217,0.0009070634,0.002728102,0.002433746,0.00122497,0.003324623,0.002741928,0.002670155,0.003735832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604873,"about_ca_system_score_gemma":0.001985339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003550268,"about_ca_topic_score_gemma":0.006197646,"domain_scores_codex":[0.9973812,0.0008051147,0.0001417109,0.0009741652,0.0004860766,0.0002118091],"domain_scores_gemma":[0.9940516,0.004210832,0.0003049067,0.0005369816,0.000769763,0.0001258719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005834987,0.0002556262,0.003639033,0.0007971519,0.0001959205,0.0007491474,0.001249042,0.06330279,0.01070588,0.1293332,0.03831448,0.7508743],"study_design_scores_gemma":[0.0001014877,0.00007979454,0.00124553,0.0001208877,0.0001360568,0.0003707704,0.0002186063,0.7733312,0.01248762,0.1895315,0.02231619,0.00006024592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005506608,0.0002499405,0.9879475,0.0002745115,0.00007965316,0.0002147047,0.001047617,0.002238852,0.002440652],"genre_scores_gemma":[0.1730822,0.0004420022,0.8085427,0.0003325198,0.0003018764,0.0009727206,0.009280557,0.0006613677,0.006383941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009481852,"threshold_uncertainty_score":0.03171992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03869690680517766,"score_gpt":0.1776629811682244,"score_spread":0.1389660743630467,"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."}}