{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002845873,0.0001590202,0.0001441527,0.000272583,0.00005195692,0.000164553,0.0007511997,0.00007374392,0.000229068],"category_scores_gemma":[0.001221156,0.0001355789,0.00005318494,0.0001323573,0.00005125457,0.0001481989,0.0001640688,0.000161785,0.0003177382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001879417,"about_ca_system_score_gemma":0.0001724314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002714723,"about_ca_topic_score_gemma":0.00001755277,"domain_scores_codex":[0.9981959,0.00006975246,0.0004123505,0.0004675431,0.000644113,0.0002103311],"domain_scores_gemma":[0.9981953,0.0003671062,0.0001502865,0.0002278158,0.0009835942,0.00007592225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002713073,0.0001464206,0.002148044,0.00000684612,0.00002703665,0.00004993502,0.00019787,0.002404585,0.0001292579,0.9495808,0.0004831527,0.0447989],"study_design_scores_gemma":[0.0009401559,0.00005018398,0.005752956,0.0002302955,0.000003327434,0.000008008007,0.0000132116,0.4551494,0.0001892506,0.5329611,0.004410212,0.000291957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01904911,0.000006575593,0.9360234,0.00469883,0.002865743,0.0001404266,0.00003843513,0.0001302099,0.03704726],"genre_scores_gemma":[0.9438475,0.00001243144,0.05451508,0.0007203522,0.0004327812,0.00001657017,0.00003516411,0.000009395787,0.0004107694],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9247984,"threshold_uncertainty_score":0.5528747,"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."}}