{"id":"W2885080048","doi":"10.1109/ipdpsw.2018.00047","title":"GraphNER: Using Corpus Level Similarities and Graph Propagation for Named Entity Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Simon Fraser University","funders":"","keywords":"Conditional random field; Named-entity recognition; Computer science; Natural language processing; Artificial intelligence; Graph; Task (project management); Sequence labeling; Information retrieval; Machine learning; Theoretical computer science","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":[0.0002152135,0.00006943871,0.00007390388,0.00009616707,0.0001733998,0.0001337977,0.0001275292,0.00004260904,0.000006907776],"category_scores_gemma":[0.00004015829,0.00006500672,0.00002761321,0.0001238307,0.00006316594,0.0005316385,0.00006337055,0.00003434567,0.000002013925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001443096,"about_ca_system_score_gemma":0.00002583397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001153042,"about_ca_topic_score_gemma":0.0001341353,"domain_scores_codex":[0.9993501,0.00002188749,0.0001343923,0.0002445384,0.0001076784,0.0001414559],"domain_scores_gemma":[0.9995313,0.00003367169,0.00005117232,0.0001613906,0.0001866824,0.0000358427],"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":[0.00005341278,0.0001382656,0.007575405,0.0003028295,0.00006939439,0.000003078466,0.002752097,0.00006439484,0.0298682,0.1820726,0.001028555,0.7760718],"study_design_scores_gemma":[0.0004837215,0.0001126469,0.001220662,0.00003514232,0.00001383809,0.00001562972,0.00005144089,0.8246996,0.01942212,0.1534459,0.0002853799,0.000213921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2342068,0.00002024968,0.7648439,0.0001503825,0.0002600145,0.0001870037,0.000004132538,0.00007693223,0.0002505943],"genre_scores_gemma":[0.6367796,0.000006809945,0.3628218,0.0001892616,0.00009116312,0.000008879069,0.000003936088,0.000004038306,0.00009447947],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8246352,"threshold_uncertainty_score":0.2650897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1310879182306465,"score_gpt":0.2852154965909825,"score_spread":0.154127578360336,"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."}}