{"id":"W4382318276","doi":"10.1609/aaai.v37i13.26958","title":"Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract)","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Mitacs","keywords":"Named-entity recognition; Computer science; Adversarial system; Transformer; Domain adaptation; Artificial intelligence; Natural language processing; Adaptation (eye); Named entity; Generalization; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0009034433,0.0002101728,0.0002406737,0.0002443114,0.00026453,0.0002353688,0.001173293,0.0001054552,0.00002709957],"category_scores_gemma":[0.000305001,0.0001874044,0.0001536224,0.0008178261,0.00007992373,0.0004290163,0.0000953745,0.0001821428,0.00005344322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008934689,"about_ca_system_score_gemma":0.0001944576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001396928,"about_ca_topic_score_gemma":0.00006094665,"domain_scores_codex":[0.9978231,0.00001430931,0.0006156532,0.0005546664,0.0006066682,0.0003855683],"domain_scores_gemma":[0.9984919,0.0001359713,0.0003260137,0.0002293757,0.0007089237,0.0001077723],"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":[0.000402689,0.000500398,0.0003305304,0.0002725349,0.00006520139,0.000002288826,0.01621329,0.03460689,0.3460922,0.2230516,0.0002176551,0.3782447],"study_design_scores_gemma":[0.00009339803,0.000171224,0.0002208529,0.0002177769,0.00001734066,6.84292e-7,0.001019033,0.615261,0.2466093,0.1361048,0.00005107175,0.0002335731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4550778,0.000001739319,0.541391,0.001652839,0.0006169124,0.0008140813,0.00001678686,0.000106414,0.0003224174],"genre_scores_gemma":[0.9542581,0.000003939307,0.04533707,0.0001751648,0.0001069304,0.00008030476,0.000004310768,0.00001354454,0.00002057245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5806541,"threshold_uncertainty_score":0.7642128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1947339476915467,"score_gpt":0.3362880907683777,"score_spread":0.141554143076831,"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."}}