{"id":"W2906879633","doi":"10.4000/books.aaccademia.4595","title":"Predicting Emoji Exploiting Multimodal Data: FBK Participation in ITAmoji Task","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Digital Communication and Language","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Emoji; Task (project management); Ranking (information retrieval); Computer science; Set (abstract data type); Natural language processing; Training set; Artificial intelligence; Information retrieval; Machine learning; World Wide Web; Engineering; Social media","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001218406,0.001810057,0.0007396836,0.001584423,0.0006919944,0.001078717,0.0005071276,0.001394762,0.003915538],"category_scores_gemma":[0.004441792,0.0001845847,0.0004654229,0.0008854522,0.0002047522,0.001291501,0.001033749,0.001185757,0.006282554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002841192,"about_ca_system_score_gemma":0.0003061943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003251816,"about_ca_topic_score_gemma":0.008578438,"domain_scores_codex":[0.999227,0.0002459602,0.00003164065,0.0001874467,0.0001600477,0.0001478145],"domain_scores_gemma":[0.9985321,0.0006707199,0.0001225459,0.0002098012,0.0002726521,0.0001920458],"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":[0.003111426,0.00134233,0.1851545,0.001100572,0.0005645739,0.001564818,0.001692227,0.02067048,0.06583653,0.001157676,0.1172519,0.600553],"study_design_scores_gemma":[0.0001023558,0.001043443,0.4318971,0.0002339414,0.00051024,0.001389903,0.002710787,0.4605478,0.03993915,0.003571107,0.05777113,0.0002830538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9319879,0.002216405,0.0249206,0.0007601648,0.0007877094,0.0001474684,0.01143569,0.003609209,0.02413487],"genre_scores_gemma":[0.9391142,0.0004576165,0.01730838,0.0002154283,0.0004728182,0.0001360819,0.02321364,0.0002618198,0.01882005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003915538,"threshold_uncertainty_score":0.01309884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07141027323031372,"score_gpt":0.2800316140296888,"score_spread":0.2086213407993751,"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."}}