{"id":"W2759598007","doi":"","title":"UQAM-NTL: Named entity recognition in Twitter messages.","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Conditional random field; Named-entity recognition; Computer science; Task (project management); Conjunction (astronomy); Artificial intelligence; Natural language processing; Named entity; Entity linking; Information retrieval; Machine learning; Knowledge base; Engineering","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":[0.001753006,0.001605791,0.001046584,0.003528419,0.000901217,0.001548775,0.001795361,0.00142098,0.02592803],"category_scores_gemma":[0.008590053,0.0004967052,0.000573942,0.001839298,0.0003900431,0.004887812,0.002688827,0.0009231648,0.04295679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009729012,"about_ca_system_score_gemma":0.001145538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007786554,"about_ca_topic_score_gemma":0.008304489,"domain_scores_codex":[0.998669,0.0003294323,0.0001556986,0.0003653609,0.0003653076,0.0001151668],"domain_scores_gemma":[0.9978136,0.0006607587,0.0002652151,0.0006858767,0.000425902,0.0001485513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001345118,0.0002790428,0.01029425,0.001516374,0.000162502,0.0009300897,0.000754544,0.004096952,0.03190209,0.004718136,0.6202483,0.3237525],"study_design_scores_gemma":[0.0003012942,0.000418358,0.0133215,0.0003326515,0.0001322472,0.001031351,0.0007085017,0.2813081,0.1282512,0.01159046,0.5623227,0.0002816513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.02365916,0.0009198162,0.1407515,0.001257256,0.0007275176,0.0012287,0.2528756,0.5577907,0.02078977],"genre_scores_gemma":[0.1336122,0.0006527044,0.2727251,0.0008033835,0.0003130299,0.002219393,0.5464757,0.00931306,0.03388547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02592803,"threshold_uncertainty_score":0.08673787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0937972724176686,"score_gpt":0.3230069221583068,"score_spread":0.2292096497406382,"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."}}