{"id":"W2102451297","doi":"10.1145/1068009.1068352","title":"Use of a genetic algorithm in brill's transformation-based part-of-speech tagger","year":2005,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Brill; Computer science; Transformation (genetics); Ranking (information retrieval); A priori and a posteriori; Part-of-speech tagging; Algorithm; Genetic algorithm; Point (geometry); Computation; Artificial intelligence; Part of speech; Natural language processing; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001743456,0.0006967636,0.0007239156,0.0008539692,0.0007935465,0.001226102,0.001358134,0.001569868,0.001677928],"category_scores_gemma":[0.004028265,0.0004966813,0.0007406913,0.0009105518,0.00110831,0.001350761,0.0008805609,0.001566473,0.0009722391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063149,"about_ca_system_score_gemma":0.001065666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005112899,"about_ca_topic_score_gemma":0.005732771,"domain_scores_codex":[0.9988464,0.0003943783,0.00005692383,0.0003055976,0.0003204778,0.00007628598],"domain_scores_gemma":[0.9989348,0.0005555393,0.00005876249,0.0002115681,0.0002096475,0.0000296395],"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":[0.000250729,0.0003288001,0.003942003,0.0001146546,0.0001724985,0.0004192287,0.0005221469,0.3734401,0.03510439,0.04356264,0.002666825,0.539476],"study_design_scores_gemma":[0.00006266738,0.0001671291,0.0006655463,0.00002571498,0.00008418611,0.0002009457,0.00004937515,0.9552447,0.02152308,0.01440739,0.007515292,0.0000539727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01743007,0.00005747277,0.9769945,0.0001628503,0.00004872314,0.0001070104,0.00003728244,0.001547099,0.003615115],"genre_scores_gemma":[0.174778,0.00009315344,0.8200484,0.0003044489,0.00002038083,0.0002352234,0.0001513643,0.0003081109,0.004060951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005112899,"threshold_uncertainty_score":0.01016623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898238605512586,"score_gpt":0.2619017559631206,"score_spread":0.2429193699079947,"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."}}