{"id":"W2811039307","doi":"10.1007/s13278-018-0523-0","title":"Entity linking of tweets based on dominant entity candidates","year":2018,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Topic Modeling","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Thomson Reuters (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Entity linking; Computer science; Information retrieval; Annotation; Context (archaeology); Limiting; Process (computing); Space (punctuation); Natural language processing; Named entity; Artificial intelligence; Knowledge base","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00061621,0.00009380384,0.0002891888,0.0001260265,0.0003782272,0.00008572348,0.0002410175,0.00006005691,0.00001547492],"category_scores_gemma":[0.00002031895,0.00008758204,0.0001429213,0.0008284891,0.00007242068,0.0000951071,0.0001298163,0.00006897419,0.000001457263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001884644,"about_ca_system_score_gemma":0.00003507876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002221217,"about_ca_topic_score_gemma":0.0006813363,"domain_scores_codex":[0.9989431,0.00008005143,0.000235435,0.000296536,0.0002113125,0.0002335733],"domain_scores_gemma":[0.9994072,0.00008349511,0.0001702347,0.0002136687,0.00007977584,0.00004564412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004140082,0.0001184303,0.8225748,0.0000478205,0.0009512811,0.00001103271,0.01011904,0.01200668,0.0003558411,0.01446085,0.0005435405,0.1387693],"study_design_scores_gemma":[0.0002465386,0.00005964503,0.03100766,0.00004916949,0.0002304591,1.542157e-7,0.00007603291,0.9663835,0.0003127648,0.001047175,0.0004232334,0.0001636587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5030555,0.00005367395,0.4958434,0.00008817497,0.0001543033,0.00003104618,9.530967e-7,0.00001752091,0.0007555478],"genre_scores_gemma":[0.9811968,0.000007068285,0.01811351,0.0001243807,0.0005143532,0.000001739779,0.000003691083,0.000003598772,0.00003487962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9543768,"threshold_uncertainty_score":0.3571492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306936156287621,"score_gpt":0.2553194528917092,"score_spread":0.242250091328833,"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."}}