{"id":"W3033628195","doi":"10.1109/tbdata.2020.2998770","title":"Dynamic Entity-Based Named Entity Recognition Under Unconstrained Tagging Schemes","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Big Data","topic":"Topic Modeling","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Named-entity recognition; Natural language processing; Sentence; Artificial intelligence; Word (group theory); Entity linking; Language model; Precision and recall; Named entity; Task (project management); Knowledge base; Linguistics","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":[0.002521572,0.001193732,0.001025807,0.001439457,0.0007237849,0.001801053,0.002212774,0.001677662,0.001430851],"category_scores_gemma":[0.005627289,0.0005242612,0.001239667,0.00195636,0.0008225462,0.009331742,0.002410228,0.001867598,0.002277413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006923339,"about_ca_system_score_gemma":0.0009282173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004072802,"about_ca_topic_score_gemma":0.005290989,"domain_scores_codex":[0.997794,0.0005097963,0.0002137182,0.001021235,0.0003226384,0.0001385832],"domain_scores_gemma":[0.9965301,0.001055979,0.0003385797,0.001408696,0.0005878506,0.00007884087],"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":[0.0004707496,0.0002747097,0.005512995,0.0003539926,0.000259734,0.00106899,0.000722148,0.3073741,0.04089523,0.04587005,0.01054613,0.5866511],"study_design_scores_gemma":[0.00001503803,0.00007374464,0.001211498,0.00003159907,0.00007658976,0.0003326508,0.0001010518,0.948422,0.02006242,0.02021273,0.009387717,0.00007296394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02229702,0.0004493534,0.9721168,0.0001814512,0.00007842542,0.0000731808,0.0004035901,0.002621623,0.001778587],"genre_scores_gemma":[0.4020863,0.001065839,0.5787286,0.0004039844,0.0001171558,0.0002287166,0.006466553,0.000330898,0.01057207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004072802,"threshold_uncertainty_score":0.01333553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1539677509368582,"score_gpt":0.2866244407588694,"score_spread":0.1326566898220111,"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."}}