{"id":"W4293920114","doi":"10.54097/hset.v12i.1368","title":"The Advance of Deep Learning Based Named Entity Recognition","year":2022,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Named-entity recognition; Computer science; Artificial intelligence; Deep learning; Entity linking; Named entity; Variety (cybernetics); Natural language processing; Artificial neural network; Machine learning; Knowledge base; Task (project management)","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":[0.0006568925,0.00005773694,0.00007972322,0.0004069779,0.0003475857,0.00002939865,0.0007323616,0.00002524258,0.000001208524],"category_scores_gemma":[0.0002011535,0.00004823419,0.000009877124,0.0015231,0.0001662015,0.0001658251,0.0003439149,0.0002301904,0.000001576937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000543402,"about_ca_system_score_gemma":0.0000344701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006027642,"about_ca_topic_score_gemma":0.000004532942,"domain_scores_codex":[0.9991447,0.00001763345,0.0001451566,0.0002612138,0.0002110317,0.0002202933],"domain_scores_gemma":[0.9995264,0.00009131842,0.0000524865,0.0002678499,0.00004106322,0.00002088126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004068096,0.00004262537,0.001217065,0.00001969856,0.000003345228,0.0000175865,0.0004355078,0.2580285,0.032321,0.5171421,0.000002821122,0.1907657],"study_design_scores_gemma":[0.0001219406,0.00004873898,0.0002527397,0.000009044027,6.791115e-7,0.00001004949,0.00005661341,0.9794202,0.01247833,0.00293101,0.004594505,0.00007608082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4806936,0.000538162,0.5156558,0.002112684,0.0005347817,0.0001035228,4.910997e-7,0.0002665005,0.00009446099],"genre_scores_gemma":[0.973219,0.00003369339,0.02669312,0.00000723963,0.000005282429,0.00002991085,1.578844e-7,0.00000254783,0.000009042331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7213917,"threshold_uncertainty_score":0.2673383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006035953314598795,"score_gpt":0.1996807815675929,"score_spread":0.1936448282529941,"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."}}