{"id":"W3124933699","doi":"10.1016/j.ipm.2021.102503","title":"Learning to rank implicit entities on Twitter","year":2021,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information retrieval; Relevance (law); Graph; Representation (politics); Rank (graph theory); Learning to rank; Context (archaeology); Feature (linguistics); Natural language processing; Entity linking; Artificial intelligence; Knowledge base; Ranking (information retrieval); Theoretical computer science; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001434648,0.0009958252,0.001001202,0.003610793,0.00079869,0.001764648,0.0008769961,0.001166391,0.001993416],"category_scores_gemma":[0.005831614,0.0003584953,0.0007005115,0.00289668,0.0003931352,0.003135663,0.001177956,0.001352221,0.002287163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006330739,"about_ca_system_score_gemma":0.0009338661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005934435,"about_ca_topic_score_gemma":0.01589419,"domain_scores_codex":[0.9989661,0.0003512449,0.00008157444,0.0002051603,0.0002156411,0.0001802429],"domain_scores_gemma":[0.9970654,0.001779255,0.0002572199,0.0002645647,0.0004705032,0.0001631779],"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":[0.002059334,0.001023554,0.1351111,0.000755922,0.0009097709,0.0004664368,0.0007118561,0.08611424,0.01757704,0.01540205,0.07260334,0.6672654],"study_design_scores_gemma":[0.00005720894,0.000191559,0.009301562,0.00004523503,0.0001449556,0.0001129102,0.0002993812,0.96294,0.004418539,0.01586423,0.006592695,0.0000316412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6224643,0.004845085,0.345316,0.005202845,0.0008080973,0.0002810091,0.008891652,0.00318764,0.009003473],"genre_scores_gemma":[0.9492143,0.0008692608,0.03353077,0.0001865496,0.0006847699,0.00009931384,0.008863958,0.00009062467,0.006460472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005934435,"threshold_uncertainty_score":0.01179981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515660422481049,"score_gpt":0.2522781744710504,"score_spread":0.2371215702462399,"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."}}