{"id":"W81387029","doi":"","title":"Learning to Classify Questions","year":2005,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Software portability; Computer science; Question answering; Artificial intelligence; Natural language processing; Boosting (machine learning); Machine learning; Set (abstract data type); Support vector machine; Feature (linguistics); Natural language; Training set; Questions and answers; Replicate; Linguistics; 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":[0.003693792,0.001063532,0.000970123,0.002562343,0.0007468808,0.002101714,0.001548375,0.001918858,0.004327327],"category_scores_gemma":[0.01563407,0.0003600121,0.0009615751,0.001212125,0.0004723258,0.003499454,0.001219236,0.002107322,0.003798555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008999552,"about_ca_system_score_gemma":0.001034031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002173162,"about_ca_topic_score_gemma":0.002599201,"domain_scores_codex":[0.9975134,0.0009080187,0.0001527173,0.0008138582,0.0004130226,0.0001990857],"domain_scores_gemma":[0.99032,0.00622226,0.0004464684,0.0007781739,0.001942169,0.0002908536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003673716,0.0008759584,0.02532097,0.0003157495,0.0001643472,0.00006671125,0.0005404663,0.01684684,0.01123118,0.009012408,0.02952459,0.9057335],"study_design_scores_gemma":[0.000126373,0.0005638818,0.01821654,0.000174233,0.0001579565,0.0003243157,0.0007675706,0.8696542,0.0177038,0.06797008,0.02426533,0.0000757063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2007892,0.002059882,0.7695299,0.003189559,0.0004662462,0.001041703,0.004702686,0.005060201,0.01316072],"genre_scores_gemma":[0.5909066,0.0006639711,0.3859597,0.0009969992,0.0005761377,0.0009626258,0.01223596,0.0001782964,0.007519805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004327327,"threshold_uncertainty_score":0.01953489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582030916985392,"score_gpt":0.2766250302420155,"score_spread":0.2508047210721616,"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."}}