{"id":"W4220712653","doi":"10.18280/ria.360115","title":"ADCSA-WSD: Adapted Discrete Crow Search Algorithm for Word Sense Disambiguation","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Computer science; Word (group theory); Word-sense disambiguation; Set (abstract data type); Meaning (existential); Artificial intelligence; Context (archaeology); Sequence labeling; Natural language processing; Sequence (biology); Field (mathematics); SemEval; Algorithm; Mathematics; WordNet","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":[0.001107337,0.0001996786,0.0002526157,0.0001936674,0.0007078379,0.0002060976,0.0008491051,0.0000572126,0.0001657629],"category_scores_gemma":[0.0001072879,0.0002100847,0.0001838219,0.0009644772,0.00006349895,0.0003183623,0.0004363898,0.0002440058,0.0002310264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001381273,"about_ca_system_score_gemma":0.0001231314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001435237,"about_ca_topic_score_gemma":0.00001802718,"domain_scores_codex":[0.9975335,0.0002093887,0.000504488,0.000701893,0.0004651182,0.0005856256],"domain_scores_gemma":[0.9983296,0.0003285639,0.0001342136,0.0008775537,0.000161855,0.0001682054],"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":[0.0000624881,0.0002227639,0.00006314551,0.00005308645,0.00003949457,0.00005839788,0.004270947,0.06104361,0.003476042,0.02130406,0.003619381,0.9057866],"study_design_scores_gemma":[0.00009439065,0.0002600767,0.0000334294,0.00002152308,0.000008245549,0.00006477656,0.001243766,0.9559336,0.02082481,0.002929469,0.0183055,0.000280439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004263673,0.0003148631,0.9907926,0.001125198,0.001290852,0.0008117023,0.00006307325,0.0002065113,0.001131529],"genre_scores_gemma":[0.9516274,0.00001563278,0.0423274,0.0002419764,0.0003213406,0.0004206245,0.00009663607,0.00003493613,0.00491405],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9484652,"threshold_uncertainty_score":0.8567005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05413081108919264,"score_gpt":0.2913148097576753,"score_spread":0.2371839986684827,"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."}}