{"id":"W4210351797","doi":"10.1109/rasse53195.2021.9686815","title":"A Survey of Natural Language Processing Implementation for Data Query Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan College; Langara College","funders":"Natural Sciences and Engineering Research Council of Canada; Harris","keywords":"Computer science; Online analytical processing; SQL; Data warehouse; Online transaction processing; Database; Big data; Implementation; Information retrieval; Query by Example; Data mining; Transaction processing; Web search query; Programming language; Search engine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004787529,0.00005678202,0.0001245021,0.00002306501,0.00004878685,0.00005206179,0.0002819138,0.00001302195,0.000003270986],"category_scores_gemma":[0.0001048041,0.00004646536,0.00001211375,0.0001972618,0.00001002745,0.000958827,0.000288208,0.00002475642,8.467774e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001165541,"about_ca_system_score_gemma":0.0001518288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002025105,"about_ca_topic_score_gemma":0.003301822,"domain_scores_codex":[0.9992256,0.00006558916,0.0002187827,0.000252948,0.0001219938,0.0001150443],"domain_scores_gemma":[0.9989494,0.0001032527,0.0001069692,0.0005968404,0.0002232787,0.00002024513],"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":[0.00003038649,0.0001238816,0.0109548,0.002317102,0.0001059849,0.00003919086,0.003800068,0.00008392795,0.02949745,0.1623424,0.01218114,0.7785236],"study_design_scores_gemma":[0.001449507,0.00007646155,0.02060007,0.0002672113,0.00001732371,0.00007248618,0.009046989,0.9143869,0.02879317,0.00004191697,0.02467594,0.0005719789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003348797,0.004192556,0.9915038,0.000037326,0.0002857017,0.0001597202,0.0003657765,0.00004114294,0.00006516297],"genre_scores_gemma":[0.8524089,0.000004284551,0.1455605,0.0000380438,0.00003688074,0.00001555476,0.001600089,0.000005442939,0.0003302748],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.914303,"threshold_uncertainty_score":0.3061366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06996851954237338,"score_gpt":0.3820479352539826,"score_spread":0.3120794157116092,"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."}}