{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007327975,0.0001169096,0.0001436426,0.00008486481,0.0001221063,0.0001948335,0.0003427535,0.00006499955,5.152874e-7],"category_scores_gemma":[0.00005630113,0.00009152668,0.00007017109,0.0002102908,0.00000828529,0.0002369339,0.00004746156,0.0001260663,0.000009968567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007546086,"about_ca_system_score_gemma":0.00003099682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000351511,"about_ca_topic_score_gemma":0.00003634875,"domain_scores_codex":[0.9989004,0.00002820556,0.0001667807,0.0003191296,0.0001964212,0.0003891097],"domain_scores_gemma":[0.9994522,0.00008445657,0.00007135018,0.0002195208,0.00008751582,0.00008494499],"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":[0.0003906868,0.0005883998,0.009233735,0.001272741,0.0003937226,0.0001877738,0.009289831,0.002581334,0.1134485,0.3937491,0.3043154,0.1645489],"study_design_scores_gemma":[0.0009375925,0.0001215897,0.001171212,0.00002548752,0.000009285142,0.0004870227,0.0006295768,0.9673293,0.02422618,0.0000541543,0.004553307,0.0004553437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008363063,0.0004624673,0.9768142,0.00004822253,0.00122955,0.0004605054,0.000002142488,0.0003804436,0.01223941],"genre_scores_gemma":[0.9219106,2.41218e-7,0.07222109,0.0001047062,0.0006002799,0.00001656688,0.00002122587,0.000008529032,0.005116704],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9647479,"threshold_uncertainty_score":0.373235,"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."}}