{"id":"W4385388948","doi":"10.18280/ria.370324","title":"Towards Amazigh Word Embedding: Corpus Creation and Word2Vec Models Evaluations","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Word2vec; Computer science; Word embedding; Natural language processing; Artificial intelligence; Word (group theory); Embedding; Linguistics; Philosophy","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":[0.0006849801,0.0001678021,0.0001760999,0.0002913607,0.0002658855,0.0003169212,0.0006835891,0.00009675983,0.00002552035],"category_scores_gemma":[0.0002538147,0.0001613949,0.00005628823,0.001480202,0.00008861888,0.000745172,0.0003379705,0.0001937729,0.0001401966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005963022,"about_ca_system_score_gemma":0.00007125705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004424086,"about_ca_topic_score_gemma":0.00000866729,"domain_scores_codex":[0.9984415,0.00006544755,0.0003430602,0.0005220047,0.0002916301,0.0003364173],"domain_scores_gemma":[0.9988334,0.0001638266,0.0001164467,0.000581525,0.0002040327,0.0001007792],"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":[0.000006704664,0.00004162427,0.00003924778,0.00005042401,0.00001068671,0.00002140764,0.004024124,0.02696117,0.004922976,0.189124,0.0008793684,0.7739182],"study_design_scores_gemma":[0.0000166175,0.00003074664,0.00002115382,0.00007792567,0.000005842164,0.00001619601,0.0001173959,0.7441742,0.02972257,0.2250836,0.0005788982,0.0001547896],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009029611,0.001188633,0.9844817,0.001777851,0.0002176492,0.0002983754,0.000002741268,0.001183183,0.001820291],"genre_scores_gemma":[0.8392348,0.0002438846,0.1579939,0.000125304,0.00006142823,0.00007572176,0.00001128349,0.00001744436,0.002236299],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8302051,"threshold_uncertainty_score":0.6581494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0634468073586783,"score_gpt":0.3522641990459674,"score_spread":0.2888173916872891,"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."}}