{"id":"W4390437770","doi":"10.48550/arxiv.2312.16917","title":"Unified Lattice Graph Fusion for Chinese Named Entity Recognition","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Atomic Energy of Canada Limited","keywords":"Computer science; Natural language processing; Artificial intelligence; Lexicon; Named-entity recognition; Graph; Adjacency list; Leverage (statistics); Adjacency matrix; Theoretical computer science; Task (project management); Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004431428,0.0002771999,0.0002922393,0.0003807824,0.0002136411,0.0001304191,0.001415883,0.0003078282,0.00001365064],"category_scores_gemma":[0.0001344422,0.0003173109,0.0002730153,0.0006981771,0.0000437645,0.0003889004,0.00168378,0.0004171676,0.0001100909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001329495,"about_ca_system_score_gemma":0.0001223798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002670152,"about_ca_topic_score_gemma":0.0001993048,"domain_scores_codex":[0.998005,0.000113637,0.0002240215,0.001210148,0.0001081897,0.0003390102],"domain_scores_gemma":[0.9980127,0.000221636,0.0002378795,0.001137933,0.000255697,0.0001341965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005859995,0.0008490573,0.01890383,0.002461608,0.0008526391,0.0009837055,0.003026755,0.4783863,0.0007101604,0.4594021,0.002242422,0.03159549],"study_design_scores_gemma":[0.0004775265,0.00002537087,0.001168067,0.00008651571,0.00004814433,0.000001267757,0.00002625963,0.6347006,0.00005057604,0.3629638,0.0001231202,0.0003287836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3763008,0.0000101667,0.6211709,0.0001601702,0.001206086,0.0003806886,0.00002569663,0.0004277242,0.0003177178],"genre_scores_gemma":[0.9841301,0.0001294904,0.0135422,0.00008036329,0.0001463846,0.000004695012,0.00009731988,0.00002468356,0.001844785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6078292,"threshold_uncertainty_score":0.9999279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1395579961527199,"score_gpt":0.2165424273676652,"score_spread":0.07698443121494536,"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."}}