{"id":"W4406499863","doi":"10.1109/cascon62161.2024.10837900","title":"Spelling Corrector for Turkish Product Search","year":2024,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Turkish; Spelling; Computer science; Product (mathematics); Artificial intelligence; Mathematics; Linguistics; Geometry","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":[0.00117828,0.00140286,0.0008939876,0.00253286,0.0006534502,0.00104289,0.001440548,0.0008254876,0.006539104],"category_scores_gemma":[0.006489204,0.0003319685,0.001042269,0.00173845,0.0003636632,0.001606578,0.0007605801,0.001187382,0.00774635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009779345,"about_ca_system_score_gemma":0.002465261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01653342,"about_ca_topic_score_gemma":0.0263227,"domain_scores_codex":[0.9989511,0.0002377272,0.0001339774,0.0003428088,0.0002385648,0.00009585859],"domain_scores_gemma":[0.997294,0.0008045002,0.0003710077,0.0005096475,0.0009431303,0.0000776052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001070247,0.0002753093,0.009221653,0.0004010376,0.0001453362,0.0004386875,0.0002328544,0.03404067,0.02943583,0.002377413,0.02886756,0.8934934],"study_design_scores_gemma":[0.00009709554,0.0004098855,0.005316314,0.00004689434,0.0001486481,0.0007515864,0.0001582142,0.9353392,0.03591277,0.002287477,0.01944284,0.00008913737],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1476231,0.002470185,0.7614834,0.0007700983,0.0005571553,0.0006126926,0.004491878,0.07640379,0.005587714],"genre_scores_gemma":[0.5705067,0.0007883419,0.4051909,0.0003597023,0.0001732008,0.0002729644,0.009491375,0.001296213,0.01192058],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01653342,"threshold_uncertainty_score":0.03287435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02580023622203639,"score_gpt":0.3189838370422317,"score_spread":0.2931836008201953,"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."}}