{"id":"W1493931908","doi":"10.1007/978-3-642-15754-7_74","title":"Unsupervised Morphological Analysis by Formal Analogy","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Analogy; Morpheme; Lexicon; Artificial intelligence; Natural language processing; Relation (database); Formal grammar; Formal description; Reading (process); Formal language; Linguistics; Rule-based machine translation; Algorithm; Programming language; Data mining","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.001033236,0.0006079353,0.0008119004,0.00158065,0.0003180909,0.000663086,0.00599101,0.000851409,0.00009138808],"category_scores_gemma":[0.000122474,0.0004922952,0.0002873176,0.001900311,0.000990482,0.0009443706,0.002196601,0.001954451,0.0000240766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001816573,"about_ca_system_score_gemma":0.0002932039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004617816,"about_ca_topic_score_gemma":0.0001072258,"domain_scores_codex":[0.9955851,0.00004077816,0.0005714008,0.001847121,0.001029997,0.0009256253],"domain_scores_gemma":[0.9970949,0.0003286733,0.0003113383,0.001743098,0.0002916106,0.0002304356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001239426,0.00007620292,0.0002196631,0.00003769248,0.0001114571,0.0006050661,0.0004959153,0.001140065,0.009797351,0.03449816,0.00009757798,0.9529085],"study_design_scores_gemma":[0.0003843953,0.00037339,0.0001121566,0.0001212596,0.0001321079,0.0003068444,1.06941e-7,0.3847128,0.02868017,0.5809084,0.002405694,0.001862645],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001752372,0.00142237,0.9954895,0.0007588296,0.0005628097,0.0002400734,0.0000127935,0.0005262666,0.0008121605],"genre_scores_gemma":[0.1358289,0.00002755471,0.8615602,0.002134385,0.0001834403,0.000008927937,0.00002670883,0.00002331155,0.0002065339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9510458,"threshold_uncertainty_score":0.9997529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095583782451334,"score_gpt":0.2534533791715864,"score_spread":0.242497541347073,"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."}}