{"id":"W2524313288","doi":"10.21700/ijcis.2016.109","title":"Textual Entailment for Arabic Language based on Lexical and Semantic Matching","year":2016,"lang":"en","type":"article","venue":"International Journal of Computing and Information Sciences","topic":"Topic Modeling","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Natural language processing; Arabic; Computer science; Artificial intelligence; Textual entailment; Logical consequence; Matching (statistics); Linguistics; Mathematics; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008576121,0.00004889509,0.00006903894,0.0002037009,0.00009353645,0.0002843532,0.0003466429,0.0000146679,0.000002040589],"category_scores_gemma":[0.0001114585,0.00002931406,0.00002620698,0.00004849233,0.0000498419,0.00147482,0.00006369796,0.000039132,0.0000013709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002185838,"about_ca_system_score_gemma":0.00005034865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003573288,"about_ca_topic_score_gemma":3.06965e-7,"domain_scores_codex":[0.9991373,0.00001753738,0.0002938433,0.00006846809,0.0003996305,0.00008326496],"domain_scores_gemma":[0.9992553,0.0002922907,0.0002284207,0.00004237339,0.0001401428,0.00004149186],"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":[0.00002201939,0.00001931624,0.001322195,0.00001090034,0.00001347143,0.000002179441,0.002486462,0.007722724,0.0004116661,0.1051962,0.00007419159,0.8827187],"study_design_scores_gemma":[0.0008489098,0.000233883,0.003826316,0.000248138,0.000002283581,0.00008778588,0.0005521319,0.9901513,0.0006768027,0.0025553,0.0007321158,0.0000849922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2707592,0.00001154096,0.7249221,0.00375873,0.0002315064,0.00002704985,8.552546e-7,0.000007997261,0.0002810131],"genre_scores_gemma":[0.9677634,0.00000591594,0.03121369,0.0009251533,0.00008602096,2.93406e-7,1.56933e-7,7.139667e-7,0.00000464753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9824286,"threshold_uncertainty_score":0.2742025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169735477574902,"score_gpt":0.3074368635685489,"score_spread":0.2904633158110587,"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."}}