{"id":"W2740006839","doi":"10.18653/v1/p17-1114","title":"A Local Detection Approach for Named Entity Recognition and Mention Detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Named-entity recognition; Sequence labeling; Task (project management); ENCODE; Sentence; Artificial intelligence; Fragment (logic); Encoding (memory); Forgetting; Sequence (biology); Representation (politics); Natural language processing; Pattern recognition (psychology); Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002999216,0.00006033084,0.00006239508,0.0000475797,0.0004404348,0.0002950812,0.0001608496,0.00005911897,0.000001966646],"category_scores_gemma":[0.00004823551,0.00005865303,0.00003140183,0.0000256192,0.00002569173,0.0007807615,0.00008230194,0.00005033065,0.00000405613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003901718,"about_ca_system_score_gemma":0.000006947965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001919405,"about_ca_topic_score_gemma":0.000180985,"domain_scores_codex":[0.9993876,0.00002046866,0.0001040716,0.0002822917,0.00009293694,0.0001126674],"domain_scores_gemma":[0.9995118,0.00001258431,0.00008286652,0.0002962532,0.00006036972,0.00003611626],"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":[0.000008356639,0.00001631347,0.00003029377,0.00001893461,0.000005114192,9.702787e-8,0.00005666236,0.0000233563,0.006216238,0.0003116753,0.000002396962,0.9933106],"study_design_scores_gemma":[0.0003798039,0.00005694028,0.001012877,0.000004425917,0.000007137951,0.000008555236,0.00002418551,0.9338295,0.05573532,0.008769346,0.00008858122,0.00008325517],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07943352,0.000007264343,0.9191728,0.00005294209,0.0002507625,0.0002474803,5.771889e-7,0.00009593579,0.0007387015],"genre_scores_gemma":[0.9072179,0.000003826414,0.09254384,0.00002029323,0.00005217587,0.0000588966,0.0000020558,0.000003211112,0.00009783381],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9932273,"threshold_uncertainty_score":0.3387513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06030778319924279,"score_gpt":0.2589302997170373,"score_spread":0.1986225165177945,"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."}}