{"id":"W2911307508","doi":"","title":"Using Neural Transfer Learning for Morpho-syntactic Tagging of South-Slavic Languages Tweets","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Morpho; Slavic languages; Natural language processing; Artificial intelligence; Serbian; Domain (mathematical analysis); Task (project management); Artificial neural network; Annotation; Transfer of learning; Word (group theory); Character (mathematics); Linguistics","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":[0.001044114,0.0007905309,0.0004703113,0.001911473,0.0009792136,0.001219286,0.0007300024,0.0009579403,0.003884048],"category_scores_gemma":[0.003411639,0.0002639098,0.0005878286,0.001719146,0.0003905752,0.002100342,0.001478497,0.0014041,0.004294055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007260584,"about_ca_system_score_gemma":0.0009876188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008410321,"about_ca_topic_score_gemma":0.01077089,"domain_scores_codex":[0.9994469,0.0001722192,0.00003239303,0.0001656445,0.00006238698,0.0001203097],"domain_scores_gemma":[0.9980221,0.001012372,0.0001055171,0.0002647387,0.0005101431,0.00008518008],"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":[0.001363963,0.000851122,0.01741361,0.0004157205,0.0002168732,0.0007131103,0.001067347,0.0409563,0.05608419,0.004721325,0.02469403,0.8515025],"study_design_scores_gemma":[0.00004471977,0.0001600075,0.008001256,0.00004458945,0.00009034141,0.0001011054,0.0009195418,0.9503614,0.02283086,0.01124598,0.006154269,0.00004596203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7092123,0.001032421,0.2506909,0.001424389,0.001141621,0.0003323638,0.004626378,0.01061329,0.02092622],"genre_scores_gemma":[0.9392439,0.0002212147,0.04315981,0.0001480587,0.0002025922,0.0001214037,0.007379538,0.0002878178,0.009235655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008410321,"threshold_uncertainty_score":0.01672274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075386681891916,"score_gpt":0.2752117786393743,"score_spread":0.2544579118204552,"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."}}