{"id":"W7100283491","doi":"","title":": Canada (2013)" ROBUST TREE-STRUCTURED NAMED ENTITIES RECOGNITION FROM SPEECH","year":2013,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Named-entity recognition; Entity linking; Conditional random field; Set (abstract data type); Knowledge base; Conjunction (astronomy); Question answering; Tree (set theory)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001222161,0.0005107918,0.0003764277,0.001388871,0.003041412,0.003648013,0.0009044416,0.001407158,0.05406918],"category_scores_gemma":[0.001342205,0.0002780161,0.0004021544,0.002210593,0.0009499255,0.001150931,0.0006862859,0.001024776,0.01839313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0142239,"about_ca_system_score_gemma":0.0192244,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8752568,"about_ca_topic_score_gemma":0.9458094,"domain_scores_codex":[0.9994216,0.00002746347,0.00001426426,0.0001010043,0.0003345043,0.0001011463],"domain_scores_gemma":[0.9988467,0.00006241007,0.00002827286,0.00006854609,0.0008427016,0.0001515168],"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":[0.0001168336,0.00002798753,0.0022981,0.0001192337,0.0000186544,0.0002422414,0.0001828635,0.001323811,0.00309556,0.008582932,0.8903119,0.09367985],"study_design_scores_gemma":[0.00001615564,0.00001218654,0.006950883,0.00004413676,0.000009354724,0.00006112742,0.0002350314,0.002319832,0.004260682,0.00191556,0.9841359,0.00003921302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02723053,0.01325282,0.05285837,0.07828835,0.01331712,0.0005126343,0.20202,0.01305759,0.5994624],"genre_scores_gemma":[0.06085345,0.003309621,0.02298269,0.003641105,0.0003927015,0.00006615208,0.04054809,0.001324676,0.8668816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1247432,"threshold_uncertainty_score":0.2509557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984710983998588,"score_gpt":0.1954199673362026,"score_spread":0.1655728574962167,"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."}}