{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002289503,0.00005470741,0.00005787532,0.0000411674,0.00003148046,0.00007772774,0.0002988815,0.00001979587,0.0000119018],"category_scores_gemma":[0.0001279435,0.00005096056,0.00001976939,0.0001088928,0.000002508666,0.0002425087,0.0001524283,0.00002919907,0.0001001487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004631723,"about_ca_system_score_gemma":0.00004151654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003564492,"about_ca_topic_score_gemma":0.00002234314,"domain_scores_codex":[0.9992943,0.00001465929,0.0001238927,0.0002536224,0.0001517613,0.0001617884],"domain_scores_gemma":[0.9993818,0.0000155103,0.00002534696,0.0002633663,0.0001753726,0.0001386036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007225392,0.00003506296,0.00006486464,0.000004323905,0.000003246663,6.079418e-7,0.0001953079,0.00003813841,0.0008085134,0.001571085,0.006842374,0.9904292],"study_design_scores_gemma":[0.002234218,0.0007082348,0.0003539637,0.0000211963,0.00001017405,0.000004962082,0.00008659997,0.5488629,0.03124454,0.05908066,0.3568718,0.0005207105],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03510017,0.000008129918,0.9581106,0.002621824,0.000887143,0.0003550154,0.000001539963,0.0001200839,0.002795474],"genre_scores_gemma":[0.1966314,0.00001246489,0.7926898,0.007809353,0.0002221475,0.0001605682,0.000009116674,0.00001252787,0.00245259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9899085,"threshold_uncertainty_score":0.2078111,"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."}}