{"id":"W2040916592","doi":"10.1145/2661829.2661887","title":"Robust Entity Linking via Random Walks","year":2014,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Computer science; Benchmark (surveying); Random walk; Information retrieval; Representation (politics); Context (archaeology); Knowledge base; Entity linking; Artificial intelligence; Popularity; Semantic similarity; Feature (linguistics); Base (topology); Similarity (geometry); Natural language processing; 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":[0.0004299753,0.00006453936,0.0000969286,0.00003703788,0.00007840537,0.000120657,0.0005501078,0.00003786734,0.00004542047],"category_scores_gemma":[0.00002857567,0.00005432917,0.00004280129,0.00008667714,0.000009409722,0.000258215,0.000211571,0.00008380843,0.0001170645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001337881,"about_ca_system_score_gemma":0.00001126756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007240139,"about_ca_topic_score_gemma":0.00003176562,"domain_scores_codex":[0.9992476,0.00004371353,0.000133208,0.000244157,0.000160516,0.0001708279],"domain_scores_gemma":[0.9993585,0.00006532258,0.00002985672,0.0004621434,0.0000323632,0.00005177224],"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":[0.000005979485,0.00005252354,0.003469387,0.00002901581,0.0000180914,0.000005338232,0.0006837546,0.01851175,0.001498826,0.3578834,0.0006307438,0.6172112],"study_design_scores_gemma":[0.0004493458,0.000008074073,0.000242756,0.00000697854,0.000001599186,0.000003529458,0.000001431302,0.9850424,0.0006188671,0.01002428,0.003515097,0.00008565836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007103103,0.00001413938,0.9704518,0.0004729865,0.0004579886,0.00005137291,2.905709e-8,0.0002108552,0.02123771],"genre_scores_gemma":[0.7676252,0.00000128832,0.2311229,0.0004554,0.0001284857,0.000002195372,2.883929e-7,0.000003026854,0.0006612432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9665306,"threshold_uncertainty_score":0.2215479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02463689415480816,"score_gpt":0.2140333906751774,"score_spread":0.1893964965203692,"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."}}