{"id":"W2140866712","doi":"10.1109/iciet.2007.4381322","title":"Hybrid Feature Selection Approach for Natural Language Call Routing Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Routing (electronic design automation); Feature (linguistics); Feature selection; Identification (biology); Natural language; Focus (optics); Set (abstract data type); Artificial intelligence; Natural language processing; Computer network; 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":[0.001240186,0.0008397555,0.0009235892,0.001257555,0.0004354192,0.00089048,0.001038885,0.0008784479,0.001971107],"category_scores_gemma":[0.002327968,0.0002382689,0.0007698276,0.000880989,0.0002848138,0.0007444385,0.0005402541,0.0005819419,0.000727122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004537208,"about_ca_system_score_gemma":0.0005586913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002867701,"about_ca_topic_score_gemma":0.003140427,"domain_scores_codex":[0.9988856,0.0004047454,0.00009248987,0.0001922776,0.0003214393,0.0001034878],"domain_scores_gemma":[0.9987299,0.0006656094,0.00006901577,0.00008736683,0.0004178583,0.00003015754],"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":[0.0005762342,0.0003787548,0.002252314,0.0001849929,0.0001970706,0.000337466,0.0001223797,0.09235988,0.04858746,0.00203254,0.004837305,0.8481336],"study_design_scores_gemma":[0.00005849132,0.0001496366,0.002303568,0.000007978162,0.00006091882,0.0001775855,0.0000426369,0.9774888,0.01484284,0.002315502,0.002511363,0.0000407289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04481,0.0003220348,0.9500934,0.0001804296,0.00005390707,0.0001287807,0.0002450775,0.003182471,0.0009839131],"genre_scores_gemma":[0.5666312,0.0001874022,0.42721,0.0002362693,0.0001336632,0.0005472058,0.001417698,0.000193067,0.003443471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002867701,"threshold_uncertainty_score":0.006593943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00998623738972575,"score_gpt":0.2394004547156615,"score_spread":0.2294142173259357,"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."}}