{"id":"W4293863352","doi":"10.1109/siu55565.2022.9864730","title":"Using Word Embeddings in Detection of Temporal Expressions in Turkish Texts","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Turkish; Scope (computer science); Computer science; Word (group theory); Artificial intelligence; Natural language processing; Set (abstract data type); The Internet; Field (mathematics); Speech recognition; Linguistics; Mathematics; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008745364,0.001035943,0.000396138,0.001689572,0.0002864698,0.001276608,0.0004184477,0.0006170651,0.001237709],"category_scores_gemma":[0.005617026,0.000196864,0.0005932229,0.001327654,0.0003559941,0.002884025,0.0006701024,0.0008135852,0.001510445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000387118,"about_ca_system_score_gemma":0.0005164652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002546109,"about_ca_topic_score_gemma":0.002486864,"domain_scores_codex":[0.998804,0.0003800684,0.0001761025,0.0004164698,0.0001462698,0.00007714515],"domain_scores_gemma":[0.9976229,0.00126004,0.0003750681,0.0002002082,0.0004935886,0.00004820941],"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":[0.0009675896,0.0003957321,0.03339457,0.0009505997,0.0002197799,0.000884802,0.002220672,0.02899102,0.06473495,0.004252845,0.007819058,0.8551683],"study_design_scores_gemma":[0.00004800476,0.0004944544,0.02547856,0.0002293869,0.0002182603,0.001265164,0.003123944,0.8868781,0.05708983,0.008338736,0.01671838,0.000117166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6298346,0.001978618,0.3537093,0.0006392022,0.00033448,0.0002740654,0.004160305,0.00429735,0.004772088],"genre_scores_gemma":[0.805638,0.0007359222,0.1833134,0.0001110143,0.00006319062,0.0001838904,0.007105771,0.000204327,0.002644563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002546109,"threshold_uncertainty_score":0.00506264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03696915632119951,"score_gpt":0.3151557293805765,"score_spread":0.278186573059377,"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."}}