{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002505866,0.0009410658,0.0007793705,0.0008407816,0.0004789161,0.0008037991,0.001146388,0.001056158,0.00156924],"category_scores_gemma":[0.003655443,0.0002683472,0.001109016,0.0008663825,0.0007458573,0.001511827,0.001275762,0.001557331,0.001104085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008162961,"about_ca_system_score_gemma":0.0004745326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009994798,"about_ca_topic_score_gemma":0.009950045,"domain_scores_codex":[0.9988803,0.0005122958,0.00003972384,0.0003558392,0.0001137116,0.00009807714],"domain_scores_gemma":[0.9981897,0.001026085,0.000103931,0.0003936585,0.0002325746,0.00005404162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004865082,0.0002449965,0.003331398,0.0001055239,0.0002670544,0.0005292177,0.000225017,0.6133045,0.01884127,0.006393769,0.01198518,0.3442855],"study_design_scores_gemma":[0.000007006658,0.0000336724,0.0004755265,0.00000447142,0.000012299,0.00005116303,0.0000168341,0.9918791,0.004892813,0.001573625,0.001040094,0.00001338931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1222413,0.0006194421,0.8657774,0.0007060538,0.0001992966,0.0001522563,0.0007961448,0.006059676,0.003448392],"genre_scores_gemma":[0.8105492,0.0003461191,0.1756085,0.000546259,0.0001090842,0.0001471016,0.004275638,0.0003654373,0.008052728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009994798,"threshold_uncertainty_score":0.01987326,"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."}}