{"id":"W2129909699","doi":"10.1162/coli_a_00085","title":"Learning Entailment Relations by Global Graph Structure Optimization","year":2011,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Haifa; Azrieli Foundation; Israel Science Foundation","keywords":"Logical consequence; Computer science; Textual entailment; Inference; Graph; Transitive relation; Artificial intelligence; Constraint (computer-aided design); Theoretical computer science; Natural language processing; Mathematics; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008022197,0.0001262415,0.00009294367,0.00006084517,0.0002169716,0.0001020288,0.0004229275,0.00007770752,0.00003540497],"category_scores_gemma":[0.0005576499,0.0001284343,0.0000342645,0.000375539,0.00004330838,0.00009876205,0.0001363652,0.0001834407,0.00001126413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009295539,"about_ca_system_score_gemma":0.00007746567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001327629,"about_ca_topic_score_gemma":9.622009e-7,"domain_scores_codex":[0.9990283,0.0000450446,0.0002190904,0.0002671885,0.0002824467,0.0001579526],"domain_scores_gemma":[0.9991003,0.00005870726,0.0001426376,0.000144732,0.0004851846,0.00006845385],"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":[0.000005611057,0.00006401337,0.002963841,0.0000123942,0.00002374672,0.00001181168,0.0005306072,0.2632276,0.00001526158,0.7263942,0.003558593,0.003192337],"study_design_scores_gemma":[0.0001424395,0.00006594973,0.0002837994,0.00001981221,0.00001150333,0.0000102896,0.00001187178,0.5154021,0.0002275106,0.4824274,0.001190462,0.0002068194],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001691327,0.0003477907,0.9964669,0.00004963134,0.0003751002,0.00008812803,0.00002448549,0.0005023192,0.001976538],"genre_scores_gemma":[0.4112686,0.000001683765,0.5884038,0.00009671122,0.00005389092,0.000002049486,0.0001285707,0.000005054576,0.00003970273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4110994,"threshold_uncertainty_score":0.5237399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091614832774298,"score_gpt":0.2556109568737796,"score_spread":0.2446948085460366,"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."}}