{"id":"W2396347845","doi":"","title":"UB.dmirg: Learning Textual Entailment Relationships Using Lexical Semantic Features.","year":2010,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textual entailment; Natural language processing; WordNet; Computer science; Artificial intelligence; Logical consequence; FrameNet; Parsing; Semantic role labeling; Sentence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001911457,0.001471324,0.0009225119,0.004300159,0.001125463,0.001704685,0.002375427,0.001709722,0.02627256],"category_scores_gemma":[0.009794572,0.0006164608,0.001236528,0.002059812,0.0005966099,0.005134377,0.00270958,0.001539099,0.01057076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001221768,"about_ca_system_score_gemma":0.001373521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005742442,"about_ca_topic_score_gemma":0.009644354,"domain_scores_codex":[0.9985377,0.0004663886,0.0001073645,0.0004267834,0.0003634696,0.00009842893],"domain_scores_gemma":[0.9982727,0.0009645338,0.0001322607,0.0003043214,0.0002600557,0.00006626542],"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":[0.0007622285,0.0004904623,0.003259885,0.001856737,0.0002590961,0.0006933673,0.0004831085,0.008177694,0.01285416,0.01838702,0.1625941,0.7901822],"study_design_scores_gemma":[0.0005519342,0.0007899562,0.007530586,0.0005186277,0.0004702403,0.001897373,0.0009736705,0.5420842,0.05560632,0.1288971,0.2605184,0.0001615488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04130682,0.005267583,0.7588359,0.001506809,0.0005130856,0.001775154,0.0460007,0.1165689,0.02822498],"genre_scores_gemma":[0.1707538,0.001440699,0.6976109,0.0009359975,0.0002364837,0.001119334,0.1138793,0.002428819,0.01159462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02627256,"threshold_uncertainty_score":0.08789051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01276895196904119,"score_gpt":0.2786167734804698,"score_spread":0.2658478215114286,"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."}}