{"id":"W2186748791","doi":"","title":"FRDC's Cross-lingual Entity Linking System at TAC 2013","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Ranking (information retrieval); Entity linking; Acronym; Information retrieval; Lexicon; Population; Artificial intelligence; Cluster analysis; Natural language processing; Heuristic; Knowledge base; Data mining","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":[0.006727535,0.001773557,0.001825775,0.006889612,0.002780176,0.003440085,0.003902378,0.002734369,0.02175784],"category_scores_gemma":[0.01106585,0.0009416267,0.001242539,0.004852982,0.0004812452,0.008731749,0.004381802,0.002912119,0.03416923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002137287,"about_ca_system_score_gemma":0.003636785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02861614,"about_ca_topic_score_gemma":0.02644625,"domain_scores_codex":[0.9944848,0.001198697,0.0004828644,0.001571572,0.001764187,0.0004978435],"domain_scores_gemma":[0.989703,0.001594399,0.0003342911,0.003389677,0.004432322,0.0005463123],"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":[0.0005969636,0.0006285182,0.00339882,0.0005812705,0.0002361608,0.0006075661,0.0006326243,0.003330644,0.01325597,0.005019855,0.5912963,0.3804154],"study_design_scores_gemma":[0.0004531873,0.000360784,0.00928699,0.0002061642,0.000359566,0.001833732,0.0009503616,0.1286784,0.06211739,0.008766693,0.7865095,0.0004772265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.05457045,0.003694726,0.2869267,0.002076354,0.001458882,0.001290886,0.1042834,0.4707194,0.07497917],"genre_scores_gemma":[0.1178543,0.0007510931,0.4707711,0.001185658,0.0002462192,0.0008009207,0.3707877,0.01158498,0.02601805],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02861614,"threshold_uncertainty_score":0.07278723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009312109205930782,"score_gpt":0.2486992344290475,"score_spread":0.2393871252231167,"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."}}