{"id":"W2782900468","doi":"10.5430/air.v7n1p23","title":"Combining Information Extraction and Text Segmentation methods in Greek Texts","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Computer science; Information extraction; Natural language processing; Artificial intelligence; Information retrieval; Resolution (logic); Text segmentation; Extraction (chemistry); Pattern recognition (psychology)","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":[0.003061984,0.001225236,0.001002436,0.005732377,0.0009765746,0.002273083,0.0006617022,0.001229282,0.002402505],"category_scores_gemma":[0.01075161,0.0003811624,0.0008952027,0.005274404,0.0007562998,0.003405435,0.001044207,0.0007580749,0.00260441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006217912,"about_ca_system_score_gemma":0.0008611604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567412,"about_ca_topic_score_gemma":0.00264247,"domain_scores_codex":[0.9969521,0.001263008,0.0003433501,0.0007991715,0.0005219511,0.0001204211],"domain_scores_gemma":[0.9903916,0.006834887,0.0005284267,0.0007550581,0.001385148,0.0001048101],"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":[0.0005630779,0.0001386272,0.002714703,0.002008975,0.000183841,0.0008633395,0.003066582,0.005783893,0.1267528,0.003983037,0.003782297,0.8501588],"study_design_scores_gemma":[0.0001918495,0.001149246,0.03854297,0.0009732205,0.001365958,0.00408749,0.005202039,0.1287677,0.5944526,0.02329137,0.20149,0.0004856893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1714541,0.00743909,0.791701,0.001060486,0.0004068284,0.0006352162,0.001714155,0.00948768,0.01610144],"genre_scores_gemma":[0.2224766,0.00183739,0.7652866,0.0001814896,0.000141905,0.0002282112,0.003435877,0.0007530267,0.00565899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005732377,"threshold_uncertainty_score":0.01619351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1514867879963813,"score_gpt":0.5122084057036119,"score_spread":0.3607216177072306,"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."}}