{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007616826,0.00003524166,0.00004021244,0.00001624871,0.00004774808,0.00009467029,0.000196863,0.00001976998,0.00007833033],"category_scores_gemma":[0.00004766931,0.00003450841,0.00001669174,0.0002097779,0.000005165596,0.0002878913,0.0001060598,0.00003320964,0.00009315801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002434672,"about_ca_system_score_gemma":0.0000826625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004745317,"about_ca_topic_score_gemma":0.00006060335,"domain_scores_codex":[0.9994818,0.00002272769,0.00008860479,0.0001879375,0.0001389443,0.00007997445],"domain_scores_gemma":[0.9994382,0.00001915312,0.00001746209,0.0003633716,0.0001324132,0.00002941051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.650185e-7,0.0001102071,0.005506956,0.00004562208,0.0000153575,0.00002242816,0.001090502,0.004256893,0.001497735,0.7773538,0.001208716,0.2088912],"study_design_scores_gemma":[0.0001388308,0.000004733484,0.005736226,0.00001263667,0.000002130783,0.00001011826,0.00006159613,0.9732006,0.008270107,0.01128502,0.001167885,0.0001100836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01043097,0.00004570041,0.98324,0.001880853,0.000274749,0.00002825203,9.784668e-8,0.0001076498,0.003991722],"genre_scores_gemma":[0.9415564,0.000002696792,0.05603587,0.0003545248,0.00003671977,0.000003695563,0.000002824304,0.000001641517,0.002005626],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9689437,"threshold_uncertainty_score":0.1407212,"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."}}