{"id":"W3004630545","doi":"10.48550/arxiv.1911.02085","title":"Path-Based Contextualization of Knowledge Graphs for Textual Entailment","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Contextualization; Textual entailment; Path (computing); Logical consequence; Computer science; Knowledge graph; Natural language processing; Artificial intelligence; Linguistics; Philosophy; Programming language; Interpretation (philosophy)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002584175,0.0002391581,0.0003518021,0.0002795891,0.00006448306,0.00003969071,0.001330248,0.0002277146,0.00001200944],"category_scores_gemma":[0.00003189729,0.0002803888,0.000270281,0.0002864061,0.00006080499,0.0001661948,0.0007678905,0.0001825671,0.00001915967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001559035,"about_ca_system_score_gemma":0.0003512972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005857055,"about_ca_topic_score_gemma":0.00002173709,"domain_scores_codex":[0.998414,0.00009846242,0.0002765885,0.0008758659,0.00008489803,0.0002502162],"domain_scores_gemma":[0.9979789,0.0001688556,0.0003372181,0.00111527,0.0003158482,0.00008389973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002816692,0.0001424907,0.001914742,0.0003475831,0.00005219568,0.000005623651,0.0003069624,0.2600726,0.00006531721,0.735759,0.0002073649,0.001097954],"study_design_scores_gemma":[0.0009647059,0.000090139,0.0001599644,0.0001671939,0.00004735537,2.933987e-7,0.00007170952,0.9776847,0.0006186885,0.01935301,0.0005563746,0.0002859048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07227065,0.0001150465,0.9252663,0.00003043175,0.0006015689,0.0006824296,0.00003222261,0.0001038106,0.0008975755],"genre_scores_gemma":[0.9941597,0.00003015191,0.00500363,0.00005537748,0.00002995064,0.000003021335,0.0000404621,0.00001459136,0.0006631023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9218891,"threshold_uncertainty_score":0.9999648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08607791740703345,"score_gpt":0.2107285574424209,"score_spread":0.1246506400353875,"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."}}