{"id":"W2289035630","doi":"10.1109/asru.2015.7404821","title":"Recent improvements to NeuroCRFs for named entity recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Conditional random field; Artificial intelligence; Margin (machine learning); Computer science; Pattern recognition (psychology); Sequence labeling; Feature (linguistics); Artificial neural network; Sequence (biology); Task (project management); Component (thermodynamics); Machine learning; Natural language processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006104681,0.002898052,0.001645966,0.004462883,0.001452581,0.002369898,0.005296205,0.0022319,0.01262111],"category_scores_gemma":[0.01399259,0.001037824,0.002016004,0.004817107,0.0008306275,0.00686658,0.002424399,0.003114887,0.01532655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003175788,"about_ca_system_score_gemma":0.003779778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05639606,"about_ca_topic_score_gemma":0.06903235,"domain_scores_codex":[0.9946775,0.001019581,0.00031935,0.001620218,0.002040781,0.0003225829],"domain_scores_gemma":[0.9908138,0.002802156,0.0002673998,0.003207073,0.002675233,0.0002343077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003414887,0.0002695918,0.001964072,0.0003275793,0.0002220535,0.00009870133,0.00009109389,0.04371823,0.003860479,0.007949405,0.07463138,0.8665259],"study_design_scores_gemma":[0.0000637856,0.0001403953,0.002320564,0.0001321105,0.000161794,0.0004442908,0.0001007366,0.7802622,0.01973039,0.02493304,0.1715849,0.0001259192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01753004,0.009618592,0.877147,0.002165728,0.001203876,0.0003900137,0.007761047,0.06599964,0.01818407],"genre_scores_gemma":[0.1320914,0.00454682,0.7944859,0.001658378,0.0009466215,0.0006449506,0.03054209,0.002749107,0.0323347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05639606,"threshold_uncertainty_score":0.1121356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1135837100107517,"score_gpt":0.2980345805104718,"score_spread":0.1844508704997201,"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."}}