{"id":"W4297990402","doi":"10.18280/ria.360409","title":"Deep Named Entity Recognition in Hindi Using Neural Networks","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Named-entity recognition; Computer science; Natural language processing; Artificial intelligence; Hindi; Phrase; Task (project management); Deep learning; Word (group theory); Autoencoder; Architecture; Named entity; Recurrent neural network; Entity linking; Artificial neural network; Linguistics; Knowledge base","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006561583,0.0001251394,0.0001595071,0.0001777209,0.0003060068,0.0001039585,0.0007796937,0.00004351317,0.0002377888],"category_scores_gemma":[0.0000548698,0.0001521855,0.00007495985,0.000817136,0.0000283761,0.0003608938,0.0005212948,0.0004077686,0.00003477728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001987863,"about_ca_system_score_gemma":0.00003078152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001973346,"about_ca_topic_score_gemma":0.00006077358,"domain_scores_codex":[0.9982676,0.0001829823,0.0004315427,0.0005016186,0.0002209142,0.0003953274],"domain_scores_gemma":[0.9991471,0.00009429261,0.0001192112,0.0005294015,0.000043548,0.00006643666],"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":[0.000004164714,0.00005887817,0.0006931546,0.000006391059,0.000002192573,0.00003068275,0.0009585494,0.8928889,0.0001775966,0.0009739477,0.000008423834,0.1041971],"study_design_scores_gemma":[0.00003319754,0.00003584408,0.00004025057,0.00001375869,0.000002814594,0.0000506778,0.0003635561,0.9951763,0.0007578729,0.003180818,0.0001714567,0.0001734958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2457881,0.0001979655,0.7525388,0.0002044653,0.0007903733,0.0001669529,7.811531e-7,0.00007057135,0.0002419772],"genre_scores_gemma":[0.987875,0.00001284827,0.01167755,0.0002134397,0.00008228466,0.00003291263,0.000005961296,0.0000107642,0.00008917361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7420869,"threshold_uncertainty_score":0.6205944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07522228084441177,"score_gpt":0.2758982561891394,"score_spread":0.2006759753447277,"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."}}