{"id":"W2806617565","doi":"","title":"The IBM Systems for Trilingual Entity Discovery and Linking at TAC 2015.","year":2015,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"IBM; Computer science; Natural language processing","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":[0.01108021,0.002875731,0.002552066,0.005311598,0.002182265,0.007601926,0.004554075,0.001758869,0.1303364],"category_scores_gemma":[0.0229875,0.002437695,0.001886869,0.005616312,0.001100447,0.01115731,0.006308997,0.004102303,0.1205002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001691124,"about_ca_system_score_gemma":0.004739324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01516351,"about_ca_topic_score_gemma":0.01629806,"domain_scores_codex":[0.9944531,0.001691384,0.0003735301,0.001223094,0.001766034,0.0004928891],"domain_scores_gemma":[0.9900435,0.001949743,0.0003831723,0.003954723,0.002407045,0.001261697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007092063,0.0001516558,0.0006925041,0.0002930164,0.0001834276,0.0001470468,0.0002818766,0.001141526,0.001882603,0.01070758,0.9108688,0.07294074],"study_design_scores_gemma":[0.0009236117,0.0002354011,0.002483118,0.0002194412,0.00017751,0.0003197939,0.0003630113,0.0626807,0.01068079,0.07953852,0.8421572,0.0002208335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.006451705,0.002717887,0.2861889,0.00319931,0.002378281,0.0009650178,0.1055637,0.5505619,0.04197335],"genre_scores_gemma":[0.05479417,0.0014498,0.4929108,0.0009443483,0.0008610977,0.001197898,0.3407221,0.04955057,0.05756923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1303364,"threshold_uncertainty_score":0.4360188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126383957649103,"score_gpt":0.2859319696838521,"score_spread":0.2732935739189418,"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."}}