{"id":"W3210290009","doi":"10.1145/3459637.3482135","title":"Location-Aware Named Entity Disambiguation","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Entity linking; Inference; Information retrieval; Artificial intelligence; Natural language processing; Dimension (graph theory); Embedding; Baseline (sea); Named-entity recognition; Natural language; Named entity; Knowledge base; Task (project management)","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":[0.002447736,0.001045639,0.001408977,0.00502594,0.001236421,0.002327837,0.002061962,0.001476013,0.002805616],"category_scores_gemma":[0.006970879,0.0005135177,0.001074766,0.004767042,0.0007332688,0.00731906,0.003395079,0.001438758,0.004323315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006327768,"about_ca_system_score_gemma":0.001640441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003608834,"about_ca_topic_score_gemma":0.006936874,"domain_scores_codex":[0.997769,0.0005397201,0.0002512317,0.0008221233,0.0004732978,0.0001446891],"domain_scores_gemma":[0.9950837,0.001611528,0.0005344272,0.00174476,0.0008830766,0.0001425103],"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":[0.0004655269,0.0002529696,0.01134575,0.001065448,0.0003778943,0.0007975646,0.0009306276,0.04907229,0.03125393,0.04436046,0.06132199,0.7987556],"study_design_scores_gemma":[0.00007972266,0.0001089379,0.007492595,0.0002407999,0.0004108692,0.001461963,0.001182721,0.6358805,0.08782814,0.07398834,0.1910755,0.0002499857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01854807,0.002753766,0.9550158,0.0006482269,0.0003450856,0.0001548877,0.00393836,0.01345375,0.005141905],"genre_scores_gemma":[0.2712269,0.002119745,0.6997182,0.000558927,0.0004002525,0.0001300652,0.01667031,0.0007661392,0.008409355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00502594,"threshold_uncertainty_score":0.01294506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02326683805433354,"score_gpt":0.2556029746595196,"score_spread":0.2323361366051861,"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."}}