{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000195519,0.00009121808,0.00007846071,0.0001758679,0.000181813,0.0008029549,0.0003114384,0.00002152013,0.00002266428],"category_scores_gemma":[0.00001975079,0.00009185184,0.00002674863,0.0003148091,0.000005184373,0.001559361,0.0002950678,0.00009190649,0.0003535998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005903408,"about_ca_system_score_gemma":0.00002616488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002981721,"about_ca_topic_score_gemma":4.379555e-7,"domain_scores_codex":[0.9990379,0.00001713162,0.0002603829,0.0001588398,0.0003323971,0.0001933448],"domain_scores_gemma":[0.9994909,0.000009542304,0.0000809367,0.0002621548,0.0001105847,0.00004586506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003028679,0.0000127499,0.00005112208,0.000178879,0.00001134326,0.000006781855,0.007767369,0.02926256,0.00001981536,0.02620356,0.001270392,0.9352124],"study_design_scores_gemma":[0.001015018,0.0000931877,0.003760432,0.0005631181,0.00002045052,0.00001958781,0.005373918,0.3833522,0.003018784,0.007257625,0.5948372,0.0006884485],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01508348,0.00001538641,0.9190452,0.002251711,0.0002118502,0.0001213044,1.573491e-7,0.0002201783,0.06305071],"genre_scores_gemma":[0.9225938,0.000007018598,0.06533907,0.008180067,0.00004326705,0.0000455549,0.000007366144,0.000005507395,0.003778359],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9345239,"threshold_uncertainty_score":0.7742912,"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."}}