{"id":"W2166202273","doi":"","title":"Bootstrapping a Stochastic Transducer for Arabic-English Transliteration Extraction","year":2007,"lang":"en","type":"article","venue":"Meeting of the Association for Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bootstrapping (finance); Computer science; Transducer; Scripting language; Artificial intelligence; Natural language processing; Metric (unit); Transliteration; Task (project management); Speech recognition; Similarity (geometry); Arabic; Pattern recognition (psychology); Programming language; Linguistics; Acoustics; Engineering; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001843879,0.0001185635,0.0001574573,0.0001087576,0.0002947205,0.0001090874,0.0003834029,0.0001103324,3.237761e-7],"category_scores_gemma":[0.008877081,0.0001077997,0.0001691128,0.0002723123,0.00001818872,0.0001109761,0.00002141879,0.0001322181,2.079145e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002146391,"about_ca_system_score_gemma":0.00009423139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005333234,"about_ca_topic_score_gemma":0.000007139969,"domain_scores_codex":[0.9985852,0.00004039236,0.0005023945,0.000225509,0.0004214443,0.0002250903],"domain_scores_gemma":[0.994791,0.00201069,0.0006373667,0.0001360332,0.002394087,0.00003087454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001580797,0.0002601143,0.001001163,0.0004552295,0.0001784021,5.42667e-7,0.007071889,0.2793137,0.004677092,0.6970106,0.001073092,0.008800037],"study_design_scores_gemma":[0.001140067,0.0001226485,0.001334392,0.0002758622,0.0001040861,0.000001910839,0.00006865757,0.6004904,0.01448079,0.3775532,0.004064955,0.0003630483],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001292436,0.0001137159,0.995942,0.0002525005,0.001359611,0.0005494583,0.00003563123,0.0001889923,0.0002656652],"genre_scores_gemma":[0.6566775,2.270905e-7,0.3427181,0.0000710173,0.0004186222,0.00001735127,0.00002631695,0.000009989126,0.00006089823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6553851,"threshold_uncertainty_score":0.9994715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409818308605324,"score_gpt":0.2948093699806726,"score_spread":0.2807111868946193,"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."}}