{"id":"W2605118633","doi":"10.1162/tacl_a_00047","title":"A Generative Model of Phonotactics","year":2017,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Phonotactics; Computer science; Generative grammar; Artificial intelligence; Generative model; Feature (linguistics); Natural language processing; Set (abstract data type); Probabilistic logic; Phonology; Hierarchy; Linguistics; Programming language","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":[0.0008487001,0.0006577195,0.0008159427,0.001215087,0.0006652,0.002076363,0.0019428,0.00151388,0.007475665],"category_scores_gemma":[0.00446942,0.0008622581,0.001635949,0.001217455,0.001403302,0.00320331,0.001540737,0.001966697,0.001697197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035,"about_ca_system_score_gemma":0.001033271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007385026,"about_ca_topic_score_gemma":0.009196887,"domain_scores_codex":[0.9994575,0.0001703572,0.0000269048,0.0001762317,0.0001007379,0.00006814725],"domain_scores_gemma":[0.9986345,0.0008625222,0.0001064165,0.0002135088,0.0001224327,0.00006061276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00011711,0.00007617051,0.005626055,0.0001356998,0.0001073439,0.0004244125,0.001027588,0.5389388,0.006493406,0.389137,0.006781249,0.05113518],"study_design_scores_gemma":[0.00001518102,0.00001631292,0.0005902626,0.00001902928,0.00002333888,0.0001842206,0.00003432056,0.8888471,0.0004636635,0.1063466,0.003438502,0.00002160227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04691969,0.0003855143,0.9378238,0.0009921156,0.00008644919,0.00007673486,0.001498654,0.001350321,0.01086671],"genre_scores_gemma":[0.8474216,0.0007016422,0.1324614,0.0005059533,0.0001538346,0.0003736386,0.002073537,0.0006821608,0.01562621],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007475665,"threshold_uncertainty_score":0.02500862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02717737201804962,"score_gpt":0.3050787880426549,"score_spread":0.2779014160246053,"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."}}