{"id":"W2184107510","doi":"","title":"IKOMA at TAC2011: A Method for Recognizing Textual Entailment using Lexical-level and Sentence Structure-level features","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textual entailment; Logical consequence; Natural language processing; Computer science; Sentence; Predicate (mathematical logic); Artificial intelligence; Argument (complex analysis); Matching (statistics); Word (group theory); Linguistics; Mathematics; Programming language","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":[0.001522437,0.001161014,0.0009906405,0.005380761,0.001360142,0.001944745,0.001699321,0.001420383,0.00753613],"category_scores_gemma":[0.006737158,0.0006448787,0.001283051,0.00213067,0.0005508639,0.002906139,0.001608573,0.001362129,0.00460347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008267632,"about_ca_system_score_gemma":0.00166556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004198086,"about_ca_topic_score_gemma":0.01032017,"domain_scores_codex":[0.9986365,0.0002190137,0.0001858924,0.0003584917,0.000491226,0.0001087507],"domain_scores_gemma":[0.9976918,0.0005732972,0.000301951,0.0005358131,0.0007714855,0.0001256331],"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":[0.0008649235,0.0005174382,0.01018023,0.00116144,0.0004231617,0.0009684411,0.001205991,0.003418708,0.07608419,0.01383126,0.05041022,0.840934],"study_design_scores_gemma":[0.0004721133,0.0008458178,0.0336644,0.0003141331,0.00117839,0.004382055,0.001512347,0.4749655,0.2412529,0.05358155,0.1873687,0.0004621181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03662354,0.0007655247,0.9165519,0.0002762475,0.0002054609,0.000626195,0.005385582,0.03090754,0.008658031],"genre_scores_gemma":[0.1463103,0.0002441819,0.8286776,0.0001605739,0.0001038702,0.000571103,0.01635114,0.001214238,0.006366959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00753613,"threshold_uncertainty_score":0.02521092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05183763661715379,"score_gpt":0.3178900933876772,"score_spread":0.2660524567705234,"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."}}