{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005001039,0.0003841274,0.0003030456,0.0003856398,0.0002987395,0.0005300561,0.0005947633,0.0004218569,0.001650319],"category_scores_gemma":[0.001114719,0.0001589418,0.0002385338,0.0005945751,0.0002199637,0.001438709,0.0005614223,0.0006687999,0.00104977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006621343,"about_ca_system_score_gemma":0.0003906696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101967,"about_ca_topic_score_gemma":0.01397392,"domain_scores_codex":[0.9997889,0.00005349912,0.00001288714,0.00007319885,0.00003764984,0.00003383718],"domain_scores_gemma":[0.9995853,0.0001833783,0.00002939372,0.00007013469,0.0001095898,0.00002214835],"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":[0.0005423036,0.000290117,0.004192968,0.0002567864,0.00008694716,0.0006696556,0.0005600061,0.1733301,0.05019774,0.009965386,0.02017907,0.739729],"study_design_scores_gemma":[0.00001468596,0.00008847201,0.003329107,0.00001509776,0.0000140736,0.00009617981,0.00009341459,0.9688945,0.01569003,0.005934657,0.005807071,0.00002274529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5474203,0.002644088,0.4175009,0.0008904606,0.0004509817,0.0002030317,0.003773759,0.01194239,0.01517414],"genre_scores_gemma":[0.8556451,0.0004573366,0.1322475,0.0001650202,0.00005883897,0.00008776233,0.004819245,0.00007333495,0.006445702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0101967,"threshold_uncertainty_score":0.0202747,"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."}}