{"id":"W3174851372","doi":"10.18653/v1/2021.findings-acl.236","title":"GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Higher Education Discipline Innovation Project; Canadian Institutes of Health Research; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Relationship extraction; Joint (building); Computer science; Extraction (chemistry); Information extraction; Relation (database); Information retrieval; Data mining; Artificial intelligence; Engineering; Structural engineering; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006095225,0.002239443,0.001501623,0.01080285,0.001110343,0.002655116,0.002101673,0.001879653,0.008849998],"category_scores_gemma":[0.01594771,0.0009105895,0.002002015,0.007775767,0.0005630315,0.007210221,0.004047041,0.001929842,0.009520538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009397661,"about_ca_system_score_gemma":0.004648365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005752513,"about_ca_topic_score_gemma":0.009101983,"domain_scores_codex":[0.9958418,0.0007818989,0.0007040075,0.001222502,0.001136926,0.0003128507],"domain_scores_gemma":[0.9898847,0.004447657,0.001163966,0.001970525,0.00216057,0.0003725958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003875775,0.0004027563,0.01024389,0.001770021,0.0003847715,0.001227825,0.001104248,0.005157784,0.02758429,0.0228463,0.2379737,0.6909168],"study_design_scores_gemma":[0.0002547614,0.0004669233,0.01647956,0.0005138318,0.0008641016,0.002536962,0.001232207,0.2482512,0.09368347,0.08693236,0.5484329,0.0003518044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01696394,0.002677089,0.8610432,0.002005377,0.0005544759,0.001262303,0.05194566,0.05484876,0.00869918],"genre_scores_gemma":[0.08767159,0.001778747,0.773903,0.0006270188,0.000630958,0.001360107,0.1190768,0.001960801,0.01299104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01080285,"threshold_uncertainty_score":0.03223503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143575687510175,"score_gpt":0.2413861483561028,"score_spread":0.219950391481001,"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."}}