{"id":"W2983102021","doi":"10.48550/arxiv.1911.06136","title":"KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Kepler; Embedding; Computer science; Benchmark (surveying); Representation (politics); Language model; Natural language processing; Construct (python library); ENCODE; Artificial intelligence; Programming language","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":[0.001575015,0.001717434,0.001004169,0.002157341,0.0005274568,0.001889223,0.003341998,0.001921627,0.003617654],"category_scores_gemma":[0.008708905,0.0008337033,0.001831076,0.002302414,0.0007918612,0.006954987,0.002941407,0.003888166,0.003437006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122714,"about_ca_system_score_gemma":0.001659242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006241342,"about_ca_topic_score_gemma":0.009962868,"domain_scores_codex":[0.9988917,0.0003301424,0.0000900896,0.0004399265,0.000153936,0.00009423786],"domain_scores_gemma":[0.9974584,0.001298906,0.0001767099,0.0006199466,0.0003488281,0.0000972284],"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":[0.0003068954,0.0003121032,0.00251751,0.00054112,0.0003032299,0.0003197483,0.0004343086,0.3517525,0.005892227,0.03405911,0.03278111,0.5707802],"study_design_scores_gemma":[0.00001708787,0.00003905065,0.0002099628,0.00003560224,0.0000372366,0.00007311592,0.00003879697,0.9699443,0.001932444,0.022601,0.005049955,0.00002137523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009384645,0.0007936897,0.9788791,0.0005578476,0.0001179963,0.0001369074,0.002192737,0.00623519,0.001701828],"genre_scores_gemma":[0.2922303,0.001620264,0.6680396,0.000943237,0.0002355933,0.001018279,0.02367606,0.001103938,0.01113276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006241342,"threshold_uncertainty_score":0.01241004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1069525455988214,"score_gpt":0.2532121545973456,"score_spread":0.1462596089985242,"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."}}