{"id":"W3157655844","doi":"","title":"HITS-UKP at TAC KBP 2019: Entity Discovery and Linking Track.","year":2019,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Computer science; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007164525,0.003757419,0.003266539,0.01155324,0.005346678,0.009344711,0.005995986,0.006102295,0.05631914],"category_scores_gemma":[0.03915536,0.001483439,0.001416364,0.01260557,0.001279954,0.0112072,0.006678057,0.005242414,0.08424255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002931265,"about_ca_system_score_gemma":0.007370328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07889169,"about_ca_topic_score_gemma":0.09037265,"domain_scores_codex":[0.9932609,0.001376311,0.0006620981,0.001001955,0.002991499,0.0007071052],"domain_scores_gemma":[0.975192,0.00619807,0.001169004,0.0049496,0.009753498,0.002737785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002442274,0.00009921993,0.0003867831,0.0004369822,0.00006310888,0.0001335002,0.0000902898,0.0005149972,0.000741904,0.001461783,0.9878131,0.00801407],"study_design_scores_gemma":[0.0008578624,0.0002845842,0.005714642,0.0005018399,0.0001859065,0.0004236236,0.0006586409,0.01650068,0.007585987,0.01164849,0.9554513,0.0001864918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.004564441,0.001040241,0.01003916,0.001885641,0.001511281,0.0004424605,0.9197285,0.04498371,0.01580453],"genre_scores_gemma":[0.002798632,0.0001323257,0.00839497,0.0002094139,0.0000739348,0.0001997415,0.983021,0.001109985,0.004060038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07889169,"threshold_uncertainty_score":0.1884063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004329036183774147,"score_gpt":0.2397907806608196,"score_spread":0.2354617444770455,"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."}}