{"id":"W2396598687","doi":"","title":"Effects of Using Simple Semantic Similarity on Textual Entailment Recognition.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textual entailment; WordNet; Logical consequence; Natural language processing; Computer science; Artificial intelligence; Semantic similarity; Task (project management); Similarity (geometry); Simple (philosophy); Baseline (sea); Image (mathematics)","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":[0.01140703,0.002776545,0.002249548,0.002783939,0.001232313,0.002459941,0.002287974,0.002895979,0.003408998],"category_scores_gemma":[0.09354513,0.0007523922,0.001233325,0.003136204,0.001395577,0.008833133,0.00388875,0.002467619,0.001540696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008893399,"about_ca_system_score_gemma":0.001209759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005375099,"about_ca_topic_score_gemma":0.007546977,"domain_scores_codex":[0.984937,0.006904847,0.001952688,0.002796449,0.002836142,0.0005728778],"domain_scores_gemma":[0.8817078,0.0987711,0.003538037,0.009315616,0.005285816,0.001381654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01498201,0.004259368,0.02092097,0.003748017,0.002248208,0.0006125966,0.0007800955,0.0455046,0.1093713,0.001909922,0.008684102,0.7869788],"study_design_scores_gemma":[0.001647661,0.01827583,0.05007424,0.0002939004,0.002400008,0.002501434,0.001662213,0.6668011,0.233283,0.01299909,0.009465406,0.0005960987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8814238,0.009393794,0.08679167,0.0008364788,0.0009914858,0.0008370845,0.001538696,0.009689138,0.008497831],"genre_scores_gemma":[0.8973261,0.0009940476,0.0956672,0.0003350263,0.0002442173,0.0002068771,0.002961908,0.0004220864,0.001842519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01140703,"threshold_uncertainty_score":0.06032687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885108430785283,"score_gpt":0.2685057563310064,"score_spread":0.2496546720231535,"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."}}