{"id":"W4306317650","doi":"10.1145/3511808.3557719","title":"Unsupervised Question Clarity Prediction through Retrieved Item Coherency","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Topic Modeling","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ambiguity; Computer science; CLARITY; Generalization; Context (archaeology); Artificial intelligence; Ask price; Graph; Machine learning; Similarity (geometry); Open domain; Information retrieval; Question answering; Natural language processing; Theoretical computer science; Mathematics","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":[0.00212268,0.0009489417,0.0008918747,0.004417122,0.0005379682,0.001197417,0.001358076,0.00138203,0.001207559],"category_scores_gemma":[0.01085958,0.0002915529,0.0008947094,0.002138843,0.0005223381,0.002986912,0.001369463,0.001271887,0.001089437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007284894,"about_ca_system_score_gemma":0.0009421289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008384282,"about_ca_topic_score_gemma":0.01646815,"domain_scores_codex":[0.9983342,0.0005522562,0.000132042,0.0005498566,0.0002954112,0.0001363325],"domain_scores_gemma":[0.9933555,0.004076265,0.0005236068,0.0005114572,0.001290825,0.0002423237],"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":[0.002371282,0.001141463,0.1393312,0.001680994,0.000816518,0.0006464964,0.003351554,0.06821698,0.06273989,0.004763937,0.02824936,0.6866903],"study_design_scores_gemma":[0.0000766503,0.000312286,0.04778342,0.00006496491,0.0002745804,0.0003283443,0.0008324197,0.9149299,0.01811444,0.008543884,0.008664644,0.00007442768],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7192603,0.005835132,0.250825,0.001049896,0.0001356773,0.0005234069,0.007562825,0.007918127,0.006889749],"genre_scores_gemma":[0.9135458,0.0004828729,0.06912764,0.0001720774,0.0001128879,0.0001732949,0.0138177,0.0001510598,0.002416671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008384282,"threshold_uncertainty_score":0.01667094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06448416126311513,"score_gpt":0.2933610998971692,"score_spread":0.2288769386340541,"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."}}