{"id":"W2908434667","doi":"10.4000/books.aaccademia.4613","title":"A Kernel-based Approach for Irony and Sarcasm Detection in Italian","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Sarcasm; Irony; Support vector machine; Artificial intelligence; Task (project management); Computer science; Kernel (algebra); Context (archaeology); Natural language processing; Machine learning; Linguistics; Mathematics; Engineering; History; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001922096,0.0002502925,0.0002654568,0.000240809,0.0001252513,0.0000580179,0.0007942872,0.000604536,0.000002256606],"category_scores_gemma":[0.00001016543,0.0003009303,0.00008914152,0.00001146528,0.000115716,0.000187167,0.0004231465,0.0004484565,0.000001300556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190005,"about_ca_system_score_gemma":0.0000789986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001061588,"about_ca_topic_score_gemma":0.00002880027,"domain_scores_codex":[0.9986474,0.00002869393,0.0001712909,0.0007206489,0.0001725716,0.0002593783],"domain_scores_gemma":[0.9991006,0.00007474998,0.0001556816,0.0005037533,0.00006238955,0.0001027932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002530288,0.00003643773,0.00003851049,0.00065447,0.0001451233,0.0000829553,0.002843668,0.0005973706,0.0003058186,0.8696365,0.001020842,0.1243852],"study_design_scores_gemma":[0.002328012,0.0001656202,0.00002851743,0.0002252959,0.00009966025,0.00001687052,0.00004806895,0.4051605,0.001333193,0.00860244,0.5809988,0.0009930526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0003343504,0.00005368422,0.6791152,0.00002435694,0.0001041577,0.0005760305,0.00001294896,0.0001196849,0.3196596],"genre_scores_gemma":[0.07603934,0.00002772475,0.07839771,0.0002483466,0.0003094026,0.000009782992,0.00001816107,0.00006966562,0.8448799],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8610341,"threshold_uncertainty_score":0.9999443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612086206838847,"score_gpt":0.2159496968584949,"score_spread":0.1798288347901064,"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."}}