{"id":"W4318775883","doi":"10.1145/3582553","title":"TME: Tree-guided Multi-task Embedding Learning towards Semantic Venue Annotation","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Wilfrid Laurier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Annotation; Information retrieval; Embedding; Feature (linguistics); Semantic similarity; Natural language processing; Artificial intelligence; Hierarchy; Tree (set theory); Exploit","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001446067,0.0001229931,0.0001813894,0.0005612656,0.001520038,0.0003118918,0.0002455732,0.0001355008,0.00008135821],"category_scores_gemma":[0.0004376377,0.0001337466,0.0001281123,0.001377332,0.00007871247,0.001184787,0.000003690659,0.0002278939,0.001497779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002390892,"about_ca_system_score_gemma":0.0001843692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005277368,"about_ca_topic_score_gemma":0.001107942,"domain_scores_codex":[0.9981405,0.0003258851,0.0005436441,0.0001494326,0.0005677587,0.0002727557],"domain_scores_gemma":[0.9988871,0.0002219976,0.0002165485,0.0002898618,0.0002840313,0.0001004752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001676264,0.00006494234,0.0005747884,0.0001942419,0.0001106478,0.000001514562,0.1094479,0.7089106,0.00008093612,0.0006209283,0.0005816027,0.179395],"study_design_scores_gemma":[0.00139449,0.000110148,0.007383763,0.0003263583,0.0001696101,0.000003439877,0.149043,0.6732612,0.0002162316,0.0001605034,0.1672794,0.0006518397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1382736,0.00004370241,0.8501539,0.001815006,0.001632334,0.0011216,0.00006999,0.001354134,0.005535668],"genre_scores_gemma":[0.9977109,0.00006478272,0.0001252675,0.00006754254,0.00009436512,0.0001646613,0.0001494173,0.000009514719,0.001613496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8594373,"threshold_uncertainty_score":0.9997798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04848614744543105,"score_gpt":0.3455220431766866,"score_spread":0.2970358957312555,"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."}}