{"id":"W6891701842","doi":"10.48448/x931-fw41","title":"An Effective, Performant Named Entity Recognition System forNoisy Business Telephone Conversation Transcripts","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stornoway Diamond (Canada)","funders":"","keywords":"Conversation; Telephony; Named-entity recognition; Noise (video); Entity linking; Named entity; Speaker recognition","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":[0.0007856506,0.0008883495,0.0005748703,0.0004878553,0.0006017456,0.0009938841,0.001249128,0.0009713743,0.006910203],"category_scores_gemma":[0.002384169,0.0003479455,0.0004390619,0.0003026677,0.0002696215,0.002040579,0.001238769,0.001152778,0.01030175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004125253,"about_ca_system_score_gemma":0.0008466299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00294109,"about_ca_topic_score_gemma":0.005502514,"domain_scores_codex":[0.9994068,0.0001296704,0.00002944615,0.0002298551,0.0001537483,0.00005051413],"domain_scores_gemma":[0.9993494,0.0001718497,0.0000294689,0.0001512838,0.0002389512,0.00005900902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000549537,0.000311717,0.001528882,0.0002647596,0.00009786661,0.0005179961,0.0006034314,0.01967529,0.2089249,0.003315819,0.05091196,0.7132978],"study_design_scores_gemma":[0.0000581203,0.0002456938,0.002109408,0.00003162527,0.00006349763,0.0004359584,0.0004005391,0.7722679,0.1761593,0.003168165,0.04495827,0.0001015171],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.03664332,0.0002935373,0.8893294,0.0003980487,0.0002433691,0.0002147393,0.001611457,0.06684755,0.004418502],"genre_scores_gemma":[0.3135957,0.0002543527,0.647091,0.000357265,0.0001099146,0.0004019421,0.0104268,0.001641252,0.02612174],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.006910203,"threshold_uncertainty_score":0.02311695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01448046842101758,"score_gpt":0.2546382423848095,"score_spread":0.240157773963792,"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."}}