{"id":"W2163571449","doi":"10.1007/978-3-319-10816-2_11","title":"A Topic Model Scoring Approach for Personalized QA Systems","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University; Université Laval","funders":"","keywords":"Mean reciprocal rank; Computer science; Latent Dirichlet allocation; Personalization; Similarity (geometry); Information retrieval; Question answering; Rank (graph theory); Set (abstract data type); Topic model; Probabilistic logic; Reciprocal; Ranking (information retrieval); Data mining; Artificial intelligence; World Wide Web","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":[0.005233496,0.001250404,0.001701144,0.002915728,0.001704696,0.002976885,0.002713137,0.002105613,0.01154977],"category_scores_gemma":[0.01630263,0.0008677974,0.001558204,0.003556348,0.0004971064,0.003952125,0.002753955,0.003118764,0.00737104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001133193,"about_ca_system_score_gemma":0.001929733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005275988,"about_ca_topic_score_gemma":0.007836175,"domain_scores_codex":[0.995449,0.001980131,0.0002953434,0.0006552395,0.001327393,0.0002928064],"domain_scores_gemma":[0.9941397,0.00263196,0.0001740442,0.001080456,0.001731703,0.0002421536],"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":[0.0003816607,0.0003263947,0.002057652,0.0003319384,0.0002328595,0.0001345692,0.0004697338,0.04560276,0.01123318,0.01761875,0.04174485,0.8798657],"study_design_scores_gemma":[0.00004614812,0.000120226,0.001323915,0.00004511235,0.0001309484,0.0002188061,0.0001132749,0.9455997,0.006358871,0.03220162,0.01377139,0.00007002193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006328967,0.0007060792,0.984705,0.0003124435,0.0001573456,0.0001922205,0.0004745304,0.00443016,0.00269344],"genre_scores_gemma":[0.2001391,0.000827761,0.7763966,0.0002703601,0.0004637529,0.0005185017,0.003850792,0.001357614,0.01617554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01154977,"threshold_uncertainty_score":0.03863782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04480884360883672,"score_gpt":0.2528119084833199,"score_spread":0.2080030648744832,"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."}}