{"id":"W3187346832","doi":"10.24963/ijcai.2021/544","title":"Hierarchical Modeling of Label Dependency and Label Noise in Fine-grained Entity Typing","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Computer science; Dependency (UML); Tree (set theory); Hierarchy; Benchmark (surveying); Artificial intelligence; Noise (video); Sentence; Natural language processing; Confusion; Machine learning","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":[0.00391255,0.001129733,0.001336111,0.002110088,0.001254212,0.001486571,0.002557235,0.001817267,0.001501548],"category_scores_gemma":[0.01183282,0.0008630242,0.001470556,0.002549102,0.001195663,0.005423707,0.002091535,0.00323303,0.0008408862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001420233,"about_ca_system_score_gemma":0.001679814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01429313,"about_ca_topic_score_gemma":0.02924433,"domain_scores_codex":[0.9981182,0.0006190427,0.0001094444,0.0006847039,0.0002545113,0.0002141492],"domain_scores_gemma":[0.9888602,0.007699032,0.0009514763,0.001226102,0.0009735092,0.0002897974],"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":[0.0007019391,0.0005020989,0.03879876,0.000395741,0.0002506606,0.000754574,0.003551263,0.5686052,0.01716013,0.0634895,0.008756305,0.2970337],"study_design_scores_gemma":[0.00000757559,0.00002158272,0.001682241,0.00001193229,0.00002490169,0.00005335466,0.00004365325,0.9759668,0.001261839,0.0201714,0.0007404189,0.00001434579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09589938,0.0005528547,0.9004149,0.0003168443,0.00005056536,0.00006881711,0.0005129995,0.001249451,0.0009342162],"genre_scores_gemma":[0.8094112,0.0004387129,0.181673,0.0002725076,0.0001499864,0.0002033707,0.002740383,0.0004654584,0.004645413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01429313,"threshold_uncertainty_score":0.02841985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03658541278609571,"score_gpt":0.2701452910301649,"score_spread":0.2335598782440692,"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."}}