{"id":"W4385571080","doi":"10.18653/v1/2023.acl-long.508","title":"Grounded Multimodal Named Entity Recognition on Social Media","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fields Institute for Research in Mathematical Sciences","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Baseline (sea); Task (project management); Construct (python library); Entity linking; Artificial intelligence; Bounding overwatch; Information retrieval; Named-entity recognition; Social media; Natural language processing; Index (typography); Graph; World Wide Web; Knowledge base; Theoretical computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799813,0.002095827,0.001211201,0.006417297,0.001237847,0.001818753,0.002001462,0.002030411,0.004750653],"category_scores_gemma":[0.00688077,0.0004176051,0.001633479,0.00466603,0.0006713709,0.009388318,0.003642668,0.001428497,0.004658956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097196,"about_ca_system_score_gemma":0.000796758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006675407,"about_ca_topic_score_gemma":0.0130298,"domain_scores_codex":[0.9973683,0.0006917293,0.0002366799,0.001066798,0.0004562086,0.0001802361],"domain_scores_gemma":[0.9958858,0.001313067,0.0004541229,0.001575666,0.0006125491,0.0001587452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008222702,0.0005710933,0.01201106,0.001551499,0.0004284996,0.00181432,0.001016651,0.02736272,0.03038877,0.02253174,0.1311625,0.770339],"study_design_scores_gemma":[0.00006463966,0.0002686849,0.01617824,0.0002726182,0.0003179085,0.001724935,0.002268921,0.6821108,0.06926814,0.05765288,0.1696338,0.0002384577],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1376496,0.007208596,0.6872911,0.002513331,0.0007897392,0.001231639,0.0891001,0.05415851,0.02005744],"genre_scores_gemma":[0.391736,0.001919623,0.4063536,0.0009351434,0.0004655857,0.000932752,0.1844338,0.0009328031,0.01229074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006675407,"threshold_uncertainty_score":0.01589245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1015538905006703,"score_gpt":0.2899108478936894,"score_spread":0.1883569573930191,"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."}}