{"id":"W2185266341","doi":"","title":"THUNLP at TAC KBP 2011 in Entity Linking","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Entity linking; Ranking (information retrieval); Pairwise comparison; Task (project management); Information retrieval; String (physics); Context (archaeology); Knowledge base; Similarity (geometry); Key (lock); Rank (graph theory); Artificial intelligence; Natural language processing; Data mining; Image (mathematics); Mathematics","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":[0.01845895,0.001452812,0.001229438,0.003638351,0.003974638,0.005323606,0.002769484,0.003299169,0.01908941],"category_scores_gemma":[0.02071846,0.000830538,0.0008730669,0.006197429,0.000887604,0.008564908,0.004195991,0.003744768,0.01899461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002290252,"about_ca_system_score_gemma":0.004261374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03128752,"about_ca_topic_score_gemma":0.03748907,"domain_scores_codex":[0.9896869,0.00407724,0.0005151763,0.001914456,0.003027451,0.0007786809],"domain_scores_gemma":[0.9849284,0.004103778,0.0004891471,0.003193717,0.005562595,0.001722385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001155928,0.001507561,0.007384573,0.0006046881,0.0002094592,0.0009800913,0.001666371,0.009384614,0.01197648,0.00723446,0.5013235,0.4565724],"study_design_scores_gemma":[0.0007050422,0.001218911,0.01245112,0.0002466693,0.0002487467,0.001212429,0.001213703,0.1239107,0.05780093,0.01511979,0.7855117,0.0003602164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1140337,0.006635064,0.6132439,0.01799633,0.01072348,0.003686222,0.0563004,0.07739778,0.09998305],"genre_scores_gemma":[0.1857283,0.002100874,0.5479183,0.002345632,0.001396587,0.001455523,0.1316466,0.005562596,0.1218458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03128752,"threshold_uncertainty_score":0.09762144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09715954466342638,"score_gpt":0.3532432388839121,"score_spread":0.2560836942204857,"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."}}