{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001471565,0.000899299,0.0006573923,0.009274587,0.001811111,0.002136289,0.0009541316,0.001146103,0.003057112],"category_scores_gemma":[0.008659597,0.0003771617,0.001103088,0.007080464,0.0003518691,0.003517602,0.001308785,0.001057695,0.001932464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004809611,"about_ca_system_score_gemma":0.0009081837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002584577,"about_ca_topic_score_gemma":0.005653752,"domain_scores_codex":[0.9980339,0.00040144,0.0001833468,0.0005929607,0.0005732989,0.0002150322],"domain_scores_gemma":[0.994354,0.003190014,0.0005565335,0.0005073384,0.001141196,0.000251002],"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":[0.002999063,0.001337562,0.2979504,0.001554178,0.001250373,0.003831504,0.004298797,0.04120706,0.07336357,0.03486718,0.0381533,0.499187],"study_design_scores_gemma":[0.00007576517,0.0004205992,0.1086035,0.0002508893,0.001303261,0.002050511,0.002360053,0.7638738,0.05187451,0.02721097,0.04180476,0.0001713784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6169688,0.002090319,0.3407709,0.0009929196,0.0005579721,0.0009677178,0.01678532,0.002097602,0.01876851],"genre_scores_gemma":[0.8762843,0.0006936372,0.09607716,0.00007109676,0.0003219809,0.0004279227,0.01925089,0.0001508572,0.006722283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009274587,"threshold_uncertainty_score":0.01022708,"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."}}