{"id":"W108353034","doi":"10.1007/978-3-642-21916-0_48","title":"Towards Automatic Acquisition of a Fully Sense Tagged Corpus for Persian","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Natural language processing; Word-sense disambiguation; Persian; Artificial intelligence; Economic shortage; SemEval; Word (group theory); Natural language understanding; Natural language; Linguistics; WordNet","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":[0.0020709,0.002011574,0.001450436,0.004746312,0.001878644,0.003221325,0.001858322,0.001482055,0.01414953],"category_scores_gemma":[0.007194425,0.00140639,0.0008130469,0.003181286,0.0009765136,0.005175275,0.005017799,0.002576463,0.016934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007568186,"about_ca_system_score_gemma":0.003200682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660882,"about_ca_topic_score_gemma":0.005322967,"domain_scores_codex":[0.9977821,0.0005336964,0.0003227151,0.0007918509,0.0003931324,0.0001765927],"domain_scores_gemma":[0.9913051,0.003034973,0.0005284013,0.001700063,0.003043493,0.0003880035],"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":[0.00110443,0.0004771339,0.005066484,0.003016543,0.0001652241,0.003660676,0.004729268,0.003972398,0.2749633,0.01844441,0.1455434,0.5388566],"study_design_scores_gemma":[0.0004460443,0.0009595234,0.02420294,0.0009101777,0.0004995062,0.007873267,0.008093698,0.09623854,0.2401919,0.04063042,0.5794963,0.0004578073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.158161,0.00284872,0.6571883,0.002047391,0.002673045,0.001087137,0.09421061,0.04765999,0.03412386],"genre_scores_gemma":[0.243655,0.001070327,0.5488427,0.0006492535,0.0006210921,0.001083616,0.1864489,0.00737848,0.01025064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01414953,"threshold_uncertainty_score":0.04733497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625536852431984,"score_gpt":0.2561624730463916,"score_spread":0.2399071045220717,"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."}}