{"id":"W2923261428","doi":"10.48550/arxiv.1903.10126","title":"Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Task (project management); Computer science; Relation (database); Code (set theory); Natural language processing; Relationship extraction; Artificial intelligence; Constant (computer programming); Data science; Programming language; Data mining; Engineering","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":[0.0001217927,0.0001404002,0.0001445923,0.0001309204,0.0001408685,0.0001001094,0.000308803,0.0001237395,0.000002815825],"category_scores_gemma":[0.00002931609,0.0001551916,0.00005820434,0.0001321075,0.00001675844,0.0003549101,0.0003919373,0.0002272673,0.000005719763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008009718,"about_ca_system_score_gemma":0.00005984776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001053616,"about_ca_topic_score_gemma":0.0001210435,"domain_scores_codex":[0.9989225,0.00006162406,0.0001123004,0.0007124785,0.0000368595,0.0001542393],"domain_scores_gemma":[0.998921,0.0001866251,0.0001690636,0.0005738824,0.00009508587,0.0000543413],"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":[0.00002199829,0.0000208026,0.001789904,0.0000744272,0.0000352535,0.00003163482,0.001638044,0.9780265,0.0001333259,0.01560304,0.000006775892,0.002618312],"study_design_scores_gemma":[0.0003774995,0.00003953198,0.0006863167,0.00004236617,0.00003854264,0.00002276021,0.0002667833,0.9961353,0.0001472462,0.002022417,0.00004148198,0.0001797437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4854521,0.00004902773,0.5136575,0.00002703574,0.0001902981,0.0002317117,0.000002677053,0.0000831636,0.0003065395],"genre_scores_gemma":[0.9915916,0.00001192491,0.007754958,0.00001261171,0.00005963566,0.00000167914,0.00001459833,0.0000124098,0.000540523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5061395,"threshold_uncertainty_score":0.6328529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0808915080268491,"score_gpt":0.2369188515485596,"score_spread":0.1560273435217105,"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."}}