{"id":"W4392669927","doi":"10.18653/v1/2023.findings-ijcnlp.4","title":"PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Supercomputing Center, Korea Institute of Science and Technology Information; Institute for Information and Communications Technology Promotion; Samsung; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Relation (database); Computer science; Calibration; Prism; Relationship extraction; Resource (disambiguation); Code (set theory); Data mining; Key (lock); Source code; Information retrieval; Perspective (graphical); Artificial intelligence; Machine learning; Statistics; Optics; Mathematics; Programming language","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":[0.004245141,0.002493551,0.001461713,0.004675126,0.0009408864,0.002715377,0.003173868,0.001931124,0.005464888],"category_scores_gemma":[0.01599634,0.0007724893,0.001993391,0.005498112,0.0008381415,0.007197787,0.003558167,0.003654286,0.009396877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197748,"about_ca_system_score_gemma":0.001900109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007690489,"about_ca_topic_score_gemma":0.01676025,"domain_scores_codex":[0.9964126,0.0009466842,0.0002330923,0.001442905,0.0007446564,0.0002201223],"domain_scores_gemma":[0.9939988,0.003056309,0.000389083,0.001567205,0.0008308848,0.0001576787],"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":[0.0003635146,0.0004457073,0.01302896,0.0008343403,0.0003663225,0.0003350626,0.0008173207,0.03928588,0.01537928,0.01049109,0.09216459,0.826488],"study_design_scores_gemma":[0.0001282874,0.0001999958,0.00881735,0.0001783465,0.0002641964,0.0008559629,0.0004029575,0.8613813,0.01920521,0.04237537,0.06605177,0.0001393607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03947224,0.004422659,0.8685505,0.001224417,0.0003754036,0.0003719802,0.009944356,0.06750155,0.008136932],"genre_scores_gemma":[0.3601965,0.001815016,0.5696136,0.001101005,0.0005831185,0.0006339284,0.04511109,0.003367782,0.01757796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007690489,"threshold_uncertainty_score":0.02245075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02968541768941629,"score_gpt":0.2561387947892135,"score_spread":0.2264533770997972,"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."}}