{"id":"W2516087440","doi":"10.18653/v1/p16-1063","title":"Generative Topic Embedding: a Continuous Representation of Documents","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"National Research Foundation","keywords":"Embedding; Computer science; Representation (politics); Generative grammar; Natural language processing; Artificial intelligence; Political science","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":[0.001557624,0.0009410367,0.0007485375,0.002502354,0.0004165689,0.00194242,0.001490668,0.001143971,0.002478397],"category_scores_gemma":[0.006944601,0.0005308219,0.001229058,0.003085768,0.000768149,0.003270117,0.001327479,0.001823043,0.00103058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000787452,"about_ca_system_score_gemma":0.0008999947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003191055,"about_ca_topic_score_gemma":0.003341409,"domain_scores_codex":[0.9988419,0.0004441382,0.00006633357,0.0003703081,0.0001981677,0.00007912559],"domain_scores_gemma":[0.9976763,0.001432738,0.0002062025,0.0003582491,0.0002542784,0.00007216717],"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":[0.0003484749,0.0001909211,0.004718134,0.000634036,0.000343224,0.0003567471,0.001432772,0.2409166,0.01212048,0.18964,0.01919507,0.5301034],"study_design_scores_gemma":[0.0000240801,0.00003896538,0.0008792675,0.00004337855,0.00003918989,0.0001440457,0.00007621439,0.9152682,0.001576986,0.0744343,0.007441924,0.00003339323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006064392,0.0006423066,0.9913279,0.0002204735,0.00005269784,0.00004046978,0.0004430599,0.0005217082,0.0006869278],"genre_scores_gemma":[0.4180459,0.002548731,0.5668688,0.0002729737,0.0004545283,0.0005464826,0.004382491,0.0005166257,0.006363319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003191055,"threshold_uncertainty_score":0.008291066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02855893766562122,"score_gpt":0.3116345904051909,"score_spread":0.2830756527395697,"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."}}