{"id":"W2408078294","doi":"","title":"Using a weakly supervised approach and lexical patterns for the KBP slot filling task.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Task (project management); Computer science; Identification (biology); Relation (database); Artificial intelligence; Natural language processing; Population; Track (disk drive); Knowledge base; Component (thermodynamics); Data mining; Engineering; Biology","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":[0.002475844,0.0009951825,0.0009117016,0.002279438,0.0009874433,0.002566454,0.001783845,0.001524476,0.003462953],"category_scores_gemma":[0.0157095,0.0005527336,0.0009717206,0.001726433,0.0006290636,0.004315999,0.00217209,0.001417573,0.005607406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004253128,"about_ca_system_score_gemma":0.001586708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002063259,"about_ca_topic_score_gemma":0.004526579,"domain_scores_codex":[0.9970388,0.001131587,0.000209891,0.000872593,0.0006364692,0.0001105998],"domain_scores_gemma":[0.9926161,0.004611596,0.0004694483,0.001164622,0.0009190419,0.0002192803],"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":[0.0006962992,0.0009246779,0.0110488,0.0006433543,0.0002866727,0.0003787918,0.002215272,0.01054927,0.09320866,0.009999464,0.01840527,0.8516434],"study_design_scores_gemma":[0.000157995,0.0005264241,0.01161018,0.0001662686,0.0002964494,0.0009433684,0.001635555,0.7834929,0.08173982,0.06613977,0.05312711,0.0001642256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02870816,0.0001003556,0.9592423,0.0002535919,0.00004358115,0.0004906285,0.0008234994,0.006759286,0.003578471],"genre_scores_gemma":[0.1718174,0.0001135259,0.8133335,0.0002090448,0.0000720829,0.0007813572,0.005782361,0.0006112526,0.007279484],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003462953,"threshold_uncertainty_score":0.01309365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06256469848337742,"score_gpt":0.267818551467447,"score_spread":0.2052538529840696,"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."}}