{"id":"W13618705","doi":"10.1007/978-3-642-38457-8_13","title":"Feature Combination for Sentence Similarity","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; SemEval; Sentence; Artificial intelligence; Task (project management); Similarity (geometry); Feature (linguistics); Feature vector; Ranking (information retrieval); Semantic similarity; Natural language processing; Pattern recognition (psychology); Support vector machine; Semantic feature","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007559934,0.0004739268,0.0004602702,0.000510775,0.0002911045,0.0006749394,0.003382171,0.0004353803,0.0000130254],"category_scores_gemma":[0.0001524151,0.0004422328,0.0001502315,0.0003439495,0.0003318379,0.0008758791,0.001050261,0.0007675416,0.00003644415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003080735,"about_ca_system_score_gemma":0.000388948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001713085,"about_ca_topic_score_gemma":0.00003185182,"domain_scores_codex":[0.9964665,0.00001982525,0.0003526791,0.001648707,0.0008593301,0.0006530259],"domain_scores_gemma":[0.9971388,0.0004193147,0.0002740084,0.001491228,0.0005163072,0.0001603205],"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":[0.00000348848,0.00003457347,0.00004798068,0.00008988624,0.00001050582,0.00001416018,0.0004333978,0.01693307,0.0001748873,0.1383253,0.0004015752,0.8435311],"study_design_scores_gemma":[0.0002274753,0.00008315284,0.00005354434,0.0001644886,0.000004428747,0.00002168632,7.252755e-8,0.7138977,0.000493884,0.2815958,0.003052187,0.0004055755],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005155811,0.0002469644,0.9888952,0.005777328,0.002301632,0.0008774265,0.000005891291,0.0001924289,0.001651511],"genre_scores_gemma":[0.0487262,0.00002582459,0.9459562,0.002752992,0.000524352,0.0000414688,0.00001091991,0.00003684183,0.001925142],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8431256,"threshold_uncertainty_score":0.9998029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427119509612694,"score_gpt":0.25115860976537,"score_spread":0.2268874146692431,"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."}}